Applied Ai Engineer
ONSITE
Los Angeles, California, United States
220,000 - 320,000
Job Title: Applied AI Engineer Company: Alfred Labs Location: Los Angeles, CA Work Arrangement: On-site, 5 days per week Salary: $220,000 - $320,000 Employment: Full-time Equity: Early-stage equity Job Description Alfred Labs is looking for an Applied AI Engineer to build deep integrations, AI agent systems, and novel approaches to agentic workflows for physical-world applications. This role combines strong software engineering fundamentals with practical experience in modern AI, including LLMs, computer vision, specialized models, and agentic systems. As an early Applied AI hire, you will have significant ownership over the company's AI direction and will help determine how AI technologies are adopted and integrated into the platform. Responsibilities - Design and build AI agents and LLM-based workflows. - Develop agent harnesses and systems with tool use. - Own the architecture and design of AI-powered systems. - Apply classical machine learning and modern AI techniques to real-world problems. - Develop and integrate LLMs, computer vision models, and specialized AI models. - Work on model post-training and fine-tuning for specialized use cases. - Build scalable AI systems using strong software engineering and distributed systems principles. - Develop evaluation and observability systems for AI applications. - Solve complex technical problems independently. - Work across multiple technical areas in a fast-moving early-stage startup. - Help shape the company's AI strategy and technical direction. Requirements - 2+ years of experience as a software engineer working on applied AI and deep learning models. - 2+ years of experience at a top Applied AI startup within the last 3 years. - Bachelor's degree in Computer Science, Mathematics, or a related field. - Hands-on experience with LLMs, computer vision, or specialized AI models. - Significant post-training or fine-tuning experience. - Strong software engineering fundamentals beyond model training. - Experience with agentic AI, tool use, reinforcement learning, or agent harnesses. - Strong understanding of classical machine learning concepts. - Ability to independently identify and solve difficult technical problems. - Comfortable working across multiple technical areas. - Willingness to work on-site in Los Angeles 4-5 days per week. Preferred Background Experience at robotics, hardware, AI infrastructure, frontier AI, or agentic AI companies is a plus. Preferred Technologies Python, PyTorch, Agent SDKs, LLMs, Computer Vision, Agentic AI, Fine-tuning, Reinforcement Learning, Evals, Observability, LangSmith, Braintrust, Weights & Biases.
Read More
APIs
Advanced SQL
Basic Statistics
Data Analysis
Data Preprocessing
Data Visualization
Django
Excel
Experimentation Techniques
Feature Engineering
JIRA
Jupyter Notebooks
Machine Learning Algorithms
Machine Learning Basics
Model Deployment
Model Evaluation
Model Training
Natural Language Processing (NLP)
NumPy
Pandas
Power BI
Python
Python Libraries
Requirements Gathering
SQL
TensorFlow
TensorFlow Basics
Version Control (Git)
AWS
Agile Methodology
Basic ML Concepts
Communication
Data Modeling
Data Visualization
Excel
Flask
Git
GitHub
Google Cloud Platform (GCP)
Kubernetes
Postman
Power BI
Process Mapping
Requirements Gathering
Scrum
Stakeholder Management
Tableau
User Experience (UX)
APIs
Advanced SQL
Basic Statistics
Data Analysis
Data Preprocessing
Data Visualization
Django
Excel
Experimentation Techniques
Feature Engineering
JIRA
Jupyter Notebooks
Machine Learning Algorithms
Machine Learning Basics
Model Deployment
Model Evaluation
Model Training
Natural Language Processing (NLP)
NumPy
Pandas
Power BI
Python
Python Libraries
Requirements Gathering
SQL
TensorFlow
TensorFlow Basics
Version Control (Git)
AWS
Agile Methodology
Basic ML Concepts
Communication
Data Modeling
Data Visualization
Excel
Flask
Git
GitHub
Google Cloud Platform (GCP)
Kubernetes
Postman
Power BI
Process Mapping
Requirements Gathering
Scrum
Stakeholder Management
Tableau
User Experience (UX)
Founding Engineer
REMOTE
San Francisco, California, United States
180,000 - 250,000
Job Title: Founding Engineer Company: Ember AI Location: Remote / San Francisco, CA Work Arrangement: Fully remote; hybrid collaboration possible for SF Bay Area candidates Salary: $180,000 - $250,000 Employment: Full-time Equity: Competitive equity Job Description Ember AI is looking for a Founding Engineer to help build its Enterprise Intelligence platform from the ground up. This is a true 0-to-1 role for an entrepreneurial, product-minded full-stack engineer who has experience building AI-powered products and thrives in ambiguity. You will work across product, infrastructure, agents, evaluations, and customer workflows, turning early customer demand into a scalable AI platform. Responsibilities - Lead development of the core platform for AI agents, including triggers, tools, approvals, evaluations, memory, observability, and workflow execution. - Own products end to end, from customer discovery and technical design through implementation, launch, and iteration. - Build core Enterprise Intelligence features across the full stack. - Develop AI-powered product features using LLM APIs, RAG, agent orchestration, and related technologies. - Shape the product roadmap and technical architecture. - Establish engineering practices and contribute to the company's technical culture. - Work directly with customers for discovery, debugging, and product feedback. - Make technical decisions and move quickly in a highly autonomous startup environment. Requirements - 3-7 years of experience in full-stack engineering. - 0-to-1 experience as a founder, founding engineer, or early-stage engineer. - Experience shipping AI-powered product features in production. - Experience working with AI labs or open-source environments is a plus. - Strong full-stack engineering capabilities. - Hands-on experience with LLM APIs, RAG, agent orchestration, agentic workflows, or evaluations. - Bachelor's degree in Computer Science or an equivalent technical degree. - Strong interest in the AI development ecosystem. - Comfortable speaking directly with customers for discovery and debugging. - High agency, strong ownership, and ability to work independently. - Comfortable working across the technology stack rather than in a narrow specialization. Preferred Technologies RAG, Agent Orchestration, Agentic Systems, Agentic Workflows, Agent Frameworks, LLM APIs, AI Agents.
Read More
RAG
Agent Orchestration
LLM APIs
Full-stack Engineering
JavaScript
HTML
CSS
Git
Node.js
React.js
RESTful APIs
Express.js
MongoDB
AWS
Microservices Architecture
DevOps Practices
Software Architecture
Agentic Workflows
AI Agents
JIRA
SQL
Postman
Agile Methodology
Figma
Docker
Test-Driven Development (TDD)
GraphQL
Kubernetes
Terraform
System Design
RAG
Agent Orchestration
LLM APIs
Full-stack Engineering
JavaScript
HTML
CSS
Git
Node.js
React.js
RESTful APIs
Express.js
MongoDB
AWS
Microservices Architecture
DevOps Practices
Software Architecture
Agentic Workflows
AI Agents
JIRA
SQL
Postman
Agile Methodology
Figma
Docker
Test-Driven Development (TDD)
GraphQL
Kubernetes
Terraform
System Design
Forward Deployed AI Engineer
HYBRID
San Diego, California, United States
165,000 - 225,000
Job Title: Staff Forward Deployed AI Engineer Company: Cadre AI Location: San Diego, CA Work Arrangement: Flexible hybrid Salary: $165,000 - $225,000 Employment: Full-time Equity: Competitive equity Job Description Cadre AI is looking for a Staff Forward Deployed AI Engineer to serve as the technical backbone of its San Diego engineering team. This is a hybrid role for an engineer who can build complex production AI systems while confidently working with executive-level clients and mentoring other engineers. Responsibilities - Build and own end-to-end production AI systems across a diverse client portfolio, including voice agents, RAG pipelines, LLM orchestration, Salesforce integrations, and automation. - Join client calls with solutions leaders and translate complex technical solutions for C-level stakeholders. - Lead technical discussions and keep client engagements on track under pressure. - Mentor and coach existing Forward Deployed Engineers. - Review engineering work and help develop the team's technical and client-facing capabilities. - Partner with the Head of Engineering on architecture, engineering processes, and technical standards. - Triage and delegate technical builds across the team. - Take ownership of the most complex client problems and drive solutions from design through production. Requirements - 5-10 years of experience as a Forward Deployed Engineer or customer-facing software engineer. - Experience designing and building client-facing AI systems in production. - Strong experience with agentic workflows, RAG pipelines, document processing, automation, and LLM orchestration. - Strong software engineering skills and experience building end-to-end production systems. - At least 1 year of experience formally mentoring or managing engineers. - Experience building voice agents tied to measurable business outcomes. - Production cloud deployment experience with AWS, GCP, or Azure. - Bachelor's degree in Computer Science, Engineering, or a STEM field. - Strong communication and executive-level client engagement skills. - High agency, low ego, and comfort working in ambiguous environments. - Based in San Diego, CA, or willing to commute from Orange County, Temecula, or Riverside. Preferred Technologies Python, Anthropic Claude, OpenAI API, LLM Orchestration, RAG, Agentic Workflows, Vector Search, Embeddings, Prompt Engineering, AWS, GCP, Azure, LangChain, LlamaIndex, Voice Agents, Salesforce Integrations.
Read More
LLM Orchestration
RAG
Voice Agents
AWS
AI Ecosystem Leadership
AI Ethics
AI Governance
AI Product Management
AI Project Management
Advanced Data Structures
Board-Level Strategy
C-Suite Leadership
Change Management Expertise
Cloud Architecture
Collaboration Platforms
Company-wide AI Strategy
Competitive Analysis
Computer Vision
Corporate Governance
Corporate Strategy Development
Cross-Functional Influence
Cross-Functional Team Leadership
Data Mining
Data Strategy
Deep Learning
Enterprise AI Solutions
Executive Communication
Executive Influence
Executive Leadership
Framework Development
Future Trends Forecasting
Global Influence
Global Strategy Execution
Industry Expertise
Industry Partnerships
Industry Standards Setting
Industry-Wide Innovation
Innovation Strategy
Innovative Business Models
Large Scale Deployments
Long-Term Vision Development
Major Transformations
Mentoring
Model Architecture
Natural Language Processing (NLP)
Organizational Architecture
Organizational Change Management
Organizational Impact
Organizational Transformation
P&L Management
Performance Management
Performance Metrics
Performance Tuning
Predictive Modeling
PyTorch
Research & Development
Risk Management
Stakeholder Engagement
Strategic AI Implementation
Strategic Vision Implementation
System Architecture
Technical Documentation
Technical Roadmap Development
Thought Leadership
Training & Development Programs
Transformational Leadership
Vision Setting
Visionary Company Strategy
Visionary Impact
Visionary Leadership
LangChain
Salesforce Integrations
Advanced SQL
Agile Project Management
Board Communication
Board-Level Advocacy
Budget Planning
Business Strategy
C-Suite Collaboration
Change Leadership
Change Management
Cost Optimization
Cross-Industry Collaboration
Cultural Competence
Diversity & Inclusion Initiatives
Emerging Technologies
Executive Presence
Global Leadership Networks
Global Market Insights
Global Policy Influence
Global Strategy
Industry Networking
Innovation Ecosystem
Innovation Frameworks
Investment Strategies
Lean Methodology
Legacy Building
Long-Term Economic Impact
Market Disruption Management
Market-Driven Strategy
Mergers & Acquisitions
Multi-Industry Engagement
OKRs
Organizational Design
Policy Development
Public Speaking
Resource Allocation
Salesforce
Strategic Mergers
Strategic Partnerships
Strategic Planning
Sustainability Initiatives
Thought Leadership
Thought Leadership Publications
Transformational Governance
User Experience (UX)
LLM Orchestration
RAG
Voice Agents
AWS
AI Ecosystem Leadership
AI Ethics
AI Governance
AI Product Management
AI Project Management
Advanced Data Structures
Board-Level Strategy
C-Suite Leadership
Change Management Expertise
Cloud Architecture
Collaboration Platforms
Company-wide AI Strategy
Competitive Analysis
Computer Vision
Corporate Governance
Corporate Strategy Development
Cross-Functional Influence
Cross-Functional Team Leadership
Data Mining
Data Strategy
Deep Learning
Enterprise AI Solutions
Executive Communication
Executive Influence
Executive Leadership
Framework Development
Future Trends Forecasting
Global Influence
Global Strategy Execution
Industry Expertise
Industry Partnerships
Industry Standards Setting
Industry-Wide Innovation
Innovation Strategy
Innovative Business Models
Large Scale Deployments
Long-Term Vision Development
Major Transformations
Mentoring
Model Architecture
Natural Language Processing (NLP)
Organizational Architecture
Organizational Change Management
Organizational Impact
Organizational Transformation
P&L Management
Performance Management
Performance Metrics
Performance Tuning
Predictive Modeling
PyTorch
Research & Development
Risk Management
Stakeholder Engagement
Strategic AI Implementation
Strategic Vision Implementation
System Architecture
Technical Documentation
Technical Roadmap Development
Thought Leadership
Training & Development Programs
Transformational Leadership
Vision Setting
Visionary Company Strategy
Visionary Impact
Visionary Leadership
LangChain
Salesforce Integrations
Advanced SQL
Agile Project Management
Board Communication
Board-Level Advocacy
Budget Planning
Business Strategy
C-Suite Collaboration
Change Leadership
Change Management
Cost Optimization
Cross-Industry Collaboration
Cultural Competence
Diversity & Inclusion Initiatives
Emerging Technologies
Executive Presence
Global Leadership Networks
Global Market Insights
Global Policy Influence
Global Strategy
Industry Networking
Innovation Ecosystem
Innovation Frameworks
Investment Strategies
Lean Methodology
Legacy Building
Long-Term Economic Impact
Market Disruption Management
Market-Driven Strategy
Mergers & Acquisitions
Multi-Industry Engagement
OKRs
Organizational Design
Policy Development
Public Speaking
Resource Allocation
Salesforce
Strategic Mergers
Strategic Partnerships
Strategic Planning
Sustainability Initiatives
Thought Leadership
Thought Leadership Publications
Transformational Governance
User Experience (UX)
Cloud Migration Consultant
ONSITE
Bangalore, Karnataka, India
Role Overview Avashya is an AI-first, platform-led services company founded by engineers and specialists with experience at AWS and Microsoft. This role owns enterprise cloud migration and modernisation work end to end: moving clients off infrastructure that holds them back and onto cloud foundations they can build on. Responsibilities • Extend migration and modernisation accelerators, including assessment tooling, landing-zone patterns, and reusable infrastructure code. Where a client estate breaks the template, build the pattern that handles it. • Turn learning from each migration into reusable reference architectures, cutover runbooks, and tested rollback paths. • Take client estates—such as monoliths, on-premise server fleets, and legacy data platforms—from discovery and dependency mapping through cutover and steady state. • Choose the right modernisation depth per workload: rehost where speed matters, replatform onto managed services, or refactor to containers or serverless where the business case supports it. • Stay accountable after cutover, including workload behaviour and cost. • Make client-specific tradeoffs between big-bang cutovers and incremental strangler approaches. • Hold workloads to cost and latency expectations throughout the move; a migration that lands slower or more expensive is a regression. • Gate go-live on measured validation of data integrity, performance parity, security posture, and cost against the pre-migration baseline. • Investigate cutover issues quickly with a rollback ready before it is needed. • Review architecture and code, and mentor as the team grows. Required Depth Migration strategy and architecture: • Practical command of the 7 Rs: knowing where rehost, replatform, and refactoring to containers or serverless are appropriate. • Cloud landing zones and multi-account foundations, including account structure, guardrails, network topology, hybrid connectivity, and when well-architected defaults fail real estates. • Discovery, dependency mapping, migration-wave sequencing, and cutovers that can be rolled back. • Data migration that preserves consistency and validation without extended downtime. Modernisation: • Compute modernisation through containerisation, monolith decomposition, and serverless where it fits. • Data and integration modernisation from legacy databases and ETL to managed and event-driven patterns. • Infrastructure as code, CI/CD for infrastructure, and repeatable environment builds. Cost, Security, and Validation: • Right-sizing and source-estate baselining to prove the migrated workload is cheaper to run. • Identity and access, encryption in transit and at rest, network segmentation, and regulated-data handling including HIPAA, GDPR, and data residency. • Validation of data integrity, performance parity, and security posture against pre-migration baselines, including P50/P95/P99 latency where relevant. Experience 2+ years of work experience with AWS Cloud Migration. Depth matters more than tenure. Compensation Top-of-market; pay reflects depth, not tenure.
Read More
AWS Cloud Migration
Cloud Landing Zones
Infrastructure as Code
CI/CD
Containerization
Serverless
Data Migration
Hybrid Connectivity
Cloud Security
Cost Optimization
Rehosting
Replatforming
Application Refactoring
AWS Well-Architected Framework
HIPAA
GDPR
AWS Cloud Migration
Cloud Landing Zones
Infrastructure as Code
CI/CD
Containerization
Serverless
Data Migration
Hybrid Connectivity
Cloud Security
Cost Optimization
Rehosting
Replatforming
Application Refactoring
AWS Well-Architected Framework
HIPAA
GDPR
Voice AI Engineer
REMOTE
Role Overview Avashya is an AI-first, platform-led services company founded by engineers and specialists with experience at AWS and Microsoft. This Voice AI Engineer role owns the real-time voice platform and every client implementation built on it—closing the gap between a demo and a production-grade voice AI product. Responsibilities • Extend the real-time voice framework; when configuration reaches its limits, work directly with the runtime. • Turn lessons from each client build into reusable platform capabilities for the next. • Take client use cases—such as outbound negotiation, multilingual support, and appointment booking—from scoping through go-live. • Tune ASR, LLM, TTS, RAG, tool-calling, and orchestration to the client, languages, and call volume rather than relying on a default stack. • Remain the technical owner after launch and diagnose production behaviour quickly. • Set latency budgets for every stage of a conversation and maintain them as load and providers change. • Gate releases on measured evaluations; investigate degraded call quality before clients need to report it. • Make client-specific tradeoffs between speed and polish; review code and mentor as the team grows. Required Depth Architecture and real-time voice systems: • Deep, hands-on knowledge of Pipecat, LiveKit Agents, or comparable orchestration frameworks, including their internals and limits. • Ability to select cascaded ASR/LLM/TTS pipelines or speech-to-speech approaches based on control, swapability, latency, and prosody needs. • Strong turn-taking expertise: VAD tuning, endpointing, barge-in, and natural handling of interruptions. • WebRTC and SIP telephony experience, including jitter buffers, PSTN versus browser tradeoffs, and Opus, PCM, and mu-law codecs. Latency, retrieval, and tool use: • Ability to decompose end-to-end latency across ASR partials, LLM time-to-first-token, TTS time-to-first-audio-byte, network hops, and tool-calling round trips. • Experience streaming ASR, LLM, and TTS together, using approaches such as fillers, early commits, or speculative synthesis when tool calls are slow. • Ability to use RAG and live tool calls while preserving a natural, live conversation. Evaluation and guardrails: • Voice-specific evaluation of ASR accuracy (WER), TTS naturalness, and end-to-end conversation quality. • Track P50, P95, and P99 latency by stage and provider over time. • Implement content safety, hallucination control, PII handling for transcripts and recordings, consent, and data-residency practices for recorded calls. Experience Depth matters more than years of experience. This role calls for senior-level ownership of production voice AI systems. Compensation Top-of-market; pay reflects depth, not tenure.
Read More
Voice AI
Pipecat
LiveKit Agents
ASR
LLM
TTS
RAG
WebRTC
SIP
VAD
Speech-to-Speech
PSTN
Voice AI Evaluation
PII Handling
Voice AI
Pipecat
LiveKit Agents
ASR
LLM
TTS
RAG
WebRTC
SIP
VAD
Speech-to-Speech
PSTN
Voice AI Evaluation
PII Handling
Ai Safety Researcher
ONSITE
Delhi, Delhi, India
Join our dynamic team as an Ai Safety Researcher, making an impactful contribution in the IT industry. You will be part of a project focused on enhancing AI safety measures, ensuring that our technological advancements align with ethical standards. Your expertise will be crucial in shaping the future of AI systems and their safe implementation. Responsibilities
Read More
RAGAS
AI Accountability Standards
AI Bias Identification
AI Ethics Assessment
AI Ethics Principles
AI Governance
AI Governance Frameworks
AI Policy Analysis
AI Policy Understanding
AI Risk Assessment
AI Safety
AI Safety Protocols
AI System Auditing
AI Transparency Tools
Advanced AI Safety Techniques
Adversarial Machine Learning
Algorithmic Transparency
Architecture Design
Basic Knowledge of AI Alignment
Basic Knowledge of AI Robustness
Complex Systems Design
Critical Thinking
Data Analysis
Data Privacy
Data Privacy Regulations
Distributed Systems
Enterprise Architecture
Ethical AI Design
Ethical Decision Making
Ethical Frameworks
Explainability (SHAP/LIME)
Fairness Metrics
Guardrails AI
Hugging Face
Human-AI Interaction Safety
Impact Assessment
LLM Red Teaming
MITRE ATLAS
Machine Learning Basics
Model Security
OWASP Top 10 for LLMs
Performance Optimization
PyTorch
Python
Research Methodologies
Responsible AI
Responsible AI Frameworks
Risk Assessment
Safety Testing Methods
Safety Verification Techniques
Standards Setting
Statistical Analysis
System Design Basics
Technical Strategy
Threat Modeling
MITRE
ATLAS
AI Ethics Workshops
Agile Methodology
Basic Knowledge of AI Safety
Basic Programming Concepts
Budget Management
Cloud Computing
Collaborative Research
Communication Skills
Conflict Resolution
Cross-Functional Leadership
Cross-Functional Teamwork
Data Visualization
Deep Learning Techniques
Documentation Skills
Ethical AI Frameworks
Ethics in Technology
Git
JIRA
Mentoring Skills
Networking Skills
Organizational Strategy
Policy Advocacy
Presentation Skills
Problem Solving
Public Speaking
Research Publication
Stakeholder Engagement
Strategic Planning
Team Collaboration
Technical Strategy
Technical Writing
TensorFlow
Vision Setting
RAGAS
AI Accountability Standards
AI Bias Identification
AI Ethics Assessment
AI Ethics Principles
AI Governance
AI Governance Frameworks
AI Policy Analysis
AI Policy Understanding
AI Risk Assessment
AI Safety
AI Safety Protocols
AI System Auditing
AI Transparency Tools
Advanced AI Safety Techniques
Adversarial Machine Learning
Algorithmic Transparency
Architecture Design
Basic Knowledge of AI Alignment
Basic Knowledge of AI Robustness
Complex Systems Design
Critical Thinking
Data Analysis
Data Privacy
Data Privacy Regulations
Distributed Systems
Enterprise Architecture
Ethical AI Design
Ethical Decision Making
Ethical Frameworks
Explainability (SHAP/LIME)
Fairness Metrics
Guardrails AI
Hugging Face
Human-AI Interaction Safety
Impact Assessment
LLM Red Teaming
MITRE ATLAS
Machine Learning Basics
Model Security
OWASP Top 10 for LLMs
Performance Optimization
PyTorch
Python
Research Methodologies
Responsible AI
Responsible AI Frameworks
Risk Assessment
Safety Testing Methods
Safety Verification Techniques
Standards Setting
Statistical Analysis
System Design Basics
Technical Strategy
Threat Modeling
MITRE
ATLAS
AI Ethics Workshops
Agile Methodology
Basic Knowledge of AI Safety
Basic Programming Concepts
Budget Management
Cloud Computing
Collaborative Research
Communication Skills
Conflict Resolution
Cross-Functional Leadership
Cross-Functional Teamwork
Data Visualization
Deep Learning Techniques
Documentation Skills
Ethical AI Frameworks
Ethics in Technology
Git
JIRA
Mentoring Skills
Networking Skills
Organizational Strategy
Policy Advocacy
Presentation Skills
Problem Solving
Public Speaking
Research Publication
Stakeholder Engagement
Strategic Planning
Team Collaboration
Technical Strategy
Technical Writing
TensorFlow
Vision Setting
Mlops Engineer
ONSITE
Delhi, Delhi, India
Join our team as an Mlops Engineer and take on the critical role of leading complex technical initiatives within the IT industry. In this position, you will be instrumental in driving architectural decisions, mentoring teams, and building scalable solutions that set industry standards. Your expertise will help shape the future of our engineering organization and influence the development of high-impact projects. Responsibilities
Read More
A/B Testing
API Development
AWS
AWS SageMaker
Advanced SQL
Architectural Design
Azure Machine Learning
Big Data Technologies
CI/CD
Cloud Cost Optimization
Configuration Management
Cross-Functional Collaboration
Data Drift
Data Governance
Data Pipelines
Data Preprocessing
Data Privacy Regulations
Django
Docker
End-to-End ML Lifecycle
Git
Google Vertex AI
Grafana
Infrastructure as Code
Jupyter Notebooks
Kubeflow
Kubernetes
Logging and Monitoring
ML Model Deployment
MLOps
MLOps Frameworks
MLOps Strategy Development
MLflow
Machine Learning Workflow
Mentoring
Model Monitoring
Model Monitoring Tools
Model Optimization
Model Performance Metrics
Model Versioning
Monitoring Tools
Performance Tuning
PostgreSQL
Prometheus
Python
Security Best Practices
Technical Leadership
TensorFlow
Terraform
Test Automation
Version Control Systems
Data Drift,
Advanced Communication Skills
Advanced Data Pipelines
Agile Methodology
Apache Airflow
Business Intelligence (BI)
Business Process Mapping
Change Management
CloudFormation
Data Lake Solutions
Data Modeling
Data Quality Assurance
Distributed Systems
Flask
GraphQL
Kubernetes
Lean Methodology
Process Automation
Scrum
Stakeholder Engagement
Stakeholder Management
Team Leadership
Technical Documentation
User Experience (UX)
Visualization Tools
A/B Testing
API Development
AWS
AWS SageMaker
Advanced SQL
Architectural Design
Azure Machine Learning
Big Data Technologies
CI/CD
Cloud Cost Optimization
Configuration Management
Cross-Functional Collaboration
Data Drift
Data Governance
Data Pipelines
Data Preprocessing
Data Privacy Regulations
Django
Docker
End-to-End ML Lifecycle
Git
Google Vertex AI
Grafana
Infrastructure as Code
Jupyter Notebooks
Kubeflow
Kubernetes
Logging and Monitoring
ML Model Deployment
MLOps
MLOps Frameworks
MLOps Strategy Development
MLflow
Machine Learning Workflow
Mentoring
Model Monitoring
Model Monitoring Tools
Model Optimization
Model Performance Metrics
Model Versioning
Monitoring Tools
Performance Tuning
PostgreSQL
Prometheus
Python
Security Best Practices
Technical Leadership
TensorFlow
Terraform
Test Automation
Version Control Systems
Data Drift,
Advanced Communication Skills
Advanced Data Pipelines
Agile Methodology
Apache Airflow
Business Intelligence (BI)
Business Process Mapping
Change Management
CloudFormation
Data Lake Solutions
Data Modeling
Data Quality Assurance
Distributed Systems
Flask
GraphQL
Kubernetes
Lean Methodology
Process Automation
Scrum
Stakeholder Engagement
Stakeholder Management
Team Leadership
Technical Documentation
User Experience (UX)
Visualization Tools
MLOps Lead
ONSITE
Delhi, Delhi, India
As an MLOps Lead in the IT industry, you will be at the forefront of driving transformational change within our engineering organization. This role involves leading complex initiatives, building world-class systems, and establishing technical standards that define our competitive advantage. You will play a crucial role in shaping the future of our engineering practices and ensuring the successful deployment of machine learning models in production environments. Responsibilities - Define and execute a multi-year technical strategy that aligns with the overall goals of the engineering organization. - Lead the architecture and design of mission-critical systems that support millions of users, ensuring scalability and reliability. - Drive the adoption of emerging technologies and establish engineering standards across multiple teams to enhance productivity and innovation. - Collaborate with C-level executives to align product roadmaps with technical feasibility assessments and strategic initiatives. - Oversee major technical initiatives, including platform modernization, scalability projects, and system migrations, ensuring timely delivery and quality. - Build and scale high-performing engineering teams through effective hiring, mentoring, and process optimization. - Represent the company at industry conferences, open-source communities, and technical advisory boards, showcasing our innovations and thought leadership. Ways of Working You will work onsite in Delhi, Delhi, India, in a dynamic and collaborative environment. This role requires strong technical leadership and the ability to communicate effectively across various levels of the organization. You will be expected to drive innovation while ensuring alignment with business objectives. Collaboration & Communication You will partner with various stakeholders, including product managers, data scientists, and business leaders, to ensure a cohesive approach to project delivery. Effective communication will be vital in presenting technical concepts to non-technical audiences and influencing decision-making at all levels. Growth Signals This role offers significant opportunities for professional development and career advancement. As a technical leader, you will have the chance to shape the engineering culture, mentor junior engineers, and influence the technical direction of the organization. Your contributions will be recognized through performance evaluations and potential promotions based on your impact on the business. Must-Haves
Read More
AI Production Systems
AWS SageMaker
Apache Airflow
Azure Machine Learning
Budget Planning
CI/CD
Change Leadership
Cloud Cost Optimization
Company-wide MLOps Impact
Compliance Standards
Continuous Improvement
Crisis Management
Cross-Functional Collaboration
Cross-Team Integration
Data Privacy Regulations
Data Strategy
Docker
Enterprise MLOps Solutions
Executive Communication
Executive Presence
Google Vertex AI
Helm
High-Level Stakeholder Engagement
Infrastructure as Code
KServe
Kubeflow
Kubernetes
MLOps
MLOps Best Practices
MLOps Strategy Development
MLflow
Mentoring
Model Governance
Model Monitoring
Organizational Change
Organizational Leadership
Performance Management
Python
Risk Management
Scalable Architecture
Strategic Planning
Technical Consulting
Technical Leadership
Technical Roadmap Development
Terraform
Thought Leadership
Vendor Management
MLOps, MLflow, Kubeflow, Kubernetes, Docker, CI/CD, Terraform, Apache Airflow, Python, AWS SageMaker, Azure Machine Learning, Google Vertex AI, Infrastructure as Code, Model Governance, Model Monitoring, KServe, Helm
Advanced Data Pipelines
Board Communication
Business Strategy Alignment
Cloud Architecture
Corporate Governance
Data Ethics
Data Quality Assurance
Global Market Trends
Industry Standards Compliance
Innovation Management
Innovation Strategy
Market Analysis
Networking Skills
Organizational Design
Research and Development
Resource Allocation
Stakeholder Engagement
Strategic Partnerships
Sustainability Initiatives
User Experience (UX)
AI Production Systems
AWS SageMaker
Apache Airflow
Azure Machine Learning
Budget Planning
CI/CD
Change Leadership
Cloud Cost Optimization
Company-wide MLOps Impact
Compliance Standards
Continuous Improvement
Crisis Management
Cross-Functional Collaboration
Cross-Team Integration
Data Privacy Regulations
Data Strategy
Docker
Enterprise MLOps Solutions
Executive Communication
Executive Presence
Google Vertex AI
Helm
High-Level Stakeholder Engagement
Infrastructure as Code
KServe
Kubeflow
Kubernetes
MLOps
MLOps Best Practices
MLOps Strategy Development
MLflow
Mentoring
Model Governance
Model Monitoring
Organizational Change
Organizational Leadership
Performance Management
Python
Risk Management
Scalable Architecture
Strategic Planning
Technical Consulting
Technical Leadership
Technical Roadmap Development
Terraform
Thought Leadership
Vendor Management
MLOps, MLflow, Kubeflow, Kubernetes, Docker, CI/CD, Terraform, Apache Airflow, Python, AWS SageMaker, Azure Machine Learning, Google Vertex AI, Infrastructure as Code, Model Governance, Model Monitoring, KServe, Helm
Advanced Data Pipelines
Board Communication
Business Strategy Alignment
Cloud Architecture
Corporate Governance
Data Ethics
Data Quality Assurance
Global Market Trends
Industry Standards Compliance
Innovation Management
Innovation Strategy
Market Analysis
Networking Skills
Organizational Design
Research and Development
Resource Allocation
Stakeholder Engagement
Strategic Partnerships
Sustainability Initiatives
User Experience (UX)
Business Analyst
ONSITE
Delhi, Delhi, India
As a Business Analyst in the IT industry, you will play a pivotal role in driving transformative projects that enhance our operational efficiencies and support strategic decision-making. Your expertise will be essential in analyzing business needs, gathering requirements, and delivering actionable insights that align with our organizational goals. You will work closely with cross-functional teams, ensuring that our technological solutions effectively meet the demands of our business landscape. Principal Responsibilities
Read More
Acceptance Criteria
Advanced Data Analytics
Advanced Data Modeling
Advanced Excel
Agile
Agile Methodology
BPMN
Basic ERP Knowledge
Basic SQL
Basic SQL Queries
Business Analysis
Business Architecture
Business Process Automation
Business Process Mapping
Business Process Reengineering
CI/CD Basics
Change Management
Complex Problem Solving
Confluence
Cross-Functional Collaboration
Data Analysis
Data Modeling
Database Design
Documentation Skills
Enterprise Architecture
Functional Requirements Analysis
JIRA
Microservices
Microsoft Excel
Oracle Cloud Basics
Oracle EBS Basics
Oracle ERP Modules
Oracle PL/SQL
Org-wide Strategy
Power BI
Problem Solving
Process Improvement
Process Mapping
Requirements Gathering
SAP Analytics Implementation
SAP Business Blueprinting
SAP Business Process Design
SAP Configuration
SAP ERP Basics
SAP FI/CO Fundamentals
SAP MM Fundamentals
SAP S/4HANA Knowledge
SAP SD Fundamentals
SQL
SQL Basics
Scrum
Stakeholder Communication
Stakeholder Management
Standards Setting
Strategic Planning
System Design Basics
System Integration
Tableau
Tech Leadership
Technical Documentation
UAT
User Acceptance Testing
User Acceptance Testing (UAT)
User Stories
Business Analysis, Requirements Gathering, Process Mapping, BPMN, User Stories, Acceptance Criteria, SQL, Advanced Excel, Power BI, Tableau, Agile, Scrum, UAT, Confluence, Jira, Data Analysis
Agile Methodology
Business Case Development
Business Intelligence Tools
Business Process Modeling Notation (BPMN)
Business Process Reengineering
Change Management
Cloud Migration
Confluence
Cross-functional Collaboration
Data Governance
Data Visualization
Excel Proficiency
Executive Communication
JIRA
Market Research
Mentoring Skills
Microsoft Excel
Microsoft Power Automate
Oracle Fusion
Organizational Change Management
Performance Metrics
Power BI
Presentation Skills
Problem Solving
Process Automation
Process Improvement
Process Mining
Requirements Analysis
Risk Assessment
Risk Management
SAP Analytics Cloud
SAP Fiori
SAP Integration Suite
SAP SD Basics
SAP Solution Manager
SQL Fundamentals
Strategic Planning
Tableau
Team Collaboration
User Experience Design
User Story Development
Acceptance Criteria
Advanced Data Analytics
Advanced Data Modeling
Advanced Excel
Agile
Agile Methodology
BPMN
Basic ERP Knowledge
Basic SQL
Basic SQL Queries
Business Analysis
Business Architecture
Business Process Automation
Business Process Mapping
Business Process Reengineering
CI/CD Basics
Change Management
Complex Problem Solving
Confluence
Cross-Functional Collaboration
Data Analysis
Data Modeling
Database Design
Documentation Skills
Enterprise Architecture
Functional Requirements Analysis
JIRA
Microservices
Microsoft Excel
Oracle Cloud Basics
Oracle EBS Basics
Oracle ERP Modules
Oracle PL/SQL
Org-wide Strategy
Power BI
Problem Solving
Process Improvement
Process Mapping
Requirements Gathering
SAP Analytics Implementation
SAP Business Blueprinting
SAP Business Process Design
SAP Configuration
SAP ERP Basics
SAP FI/CO Fundamentals
SAP MM Fundamentals
SAP S/4HANA Knowledge
SAP SD Fundamentals
SQL
SQL Basics
Scrum
Stakeholder Communication
Stakeholder Management
Standards Setting
Strategic Planning
System Design Basics
System Integration
Tableau
Tech Leadership
Technical Documentation
UAT
User Acceptance Testing
User Acceptance Testing (UAT)
User Stories
Business Analysis, Requirements Gathering, Process Mapping, BPMN, User Stories, Acceptance Criteria, SQL, Advanced Excel, Power BI, Tableau, Agile, Scrum, UAT, Confluence, Jira, Data Analysis
Agile Methodology
Business Case Development
Business Intelligence Tools
Business Process Modeling Notation (BPMN)
Business Process Reengineering
Change Management
Cloud Migration
Confluence
Cross-functional Collaboration
Data Governance
Data Visualization
Excel Proficiency
Executive Communication
JIRA
Market Research
Mentoring Skills
Microsoft Excel
Microsoft Power Automate
Oracle Fusion
Organizational Change Management
Performance Metrics
Power BI
Presentation Skills
Problem Solving
Process Automation
Process Improvement
Process Mining
Requirements Analysis
Risk Assessment
Risk Management
SAP Analytics Cloud
SAP Fiori
SAP Integration Suite
SAP SD Basics
SAP Solution Manager
SQL Fundamentals
Strategic Planning
Tableau
Team Collaboration
User Experience Design
User Story Development
AI Quality Assurance Engineer
ONSITE
Delhi, Delhi, India
As an AI Quality Assurance Engineer, you will play a pivotal role in ensuring the delivery of high-quality software products in the IT industry. You will engage in various testing activities, working closely with development teams to validate AI-driven solutions. Your expertise will contribute to enhancing product reliability and user satisfaction. Responsibilities
Read More
AI Testing
API Testing
Bias Detection
CI/CD
Data Quality
ETL Testing
Jira
Load Testing
Model Validation
Postman
Pytest
Selenium
Test Automation
AI Testing, Model Validation, Test Automation, Python, SQL, Selenium, Pytest, Postman, API Testing, ETL Testing, Performance Testing, Load Testing, Bias Detection, Data Quality, CI/CD, Jira, TestRail
Rest
Microservices
Agile Methodology
Automated Testing
Data Analysis
Defect Tracking
JIRA
Machine Learning Concepts
Mentoring Juniors
Performance Testing
Python
Quality Metrics
Requirements Gathering
Risk Assessment
SQL
Test Case Design
Test Strategy Development
API Testing
Advanced Data Analysis
Cloud Testing (AWS)
Confluence
Data Engineering Basics
Data Profiling
Excel
Scrum
TensorFlow
User Acceptance Testing (UAT)
AI Testing
API Testing
Bias Detection
CI/CD
Data Quality
ETL Testing
Jira
Load Testing
Model Validation
Postman
Pytest
Selenium
Test Automation
AI Testing, Model Validation, Test Automation, Python, SQL, Selenium, Pytest, Postman, API Testing, ETL Testing, Performance Testing, Load Testing, Bias Detection, Data Quality, CI/CD, Jira, TestRail
Rest
Microservices
Agile Methodology
Automated Testing
Data Analysis
Defect Tracking
JIRA
Machine Learning Concepts
Mentoring Juniors
Performance Testing
Python
Quality Metrics
Requirements Gathering
Risk Assessment
SQL
Test Case Design
Test Strategy Development
API Testing
Advanced Data Analysis
Cloud Testing (AWS)
Confluence
Data Engineering Basics
Data Profiling
Excel
Scrum
TensorFlow
User Acceptance Testing (UAT)