Posted 16 June, 2026
Senior AI/ML Engineer
NR Consulting - India
Bangalore, Karnataka, IN
Full Time
Reference: 26-06479-2220-1
Title: Senior AI/ML Engineer
Location: Bangalore
Exp: 6+ Years
Job Description:
Agentic AI Architecture & Development
• Design and implement multi-agent systems using:
o Azure AI Agent Service
o Microsoft Agentic Framework
• Define:
o Agent roles, memory, and orchestration patterns
o Tool/skill interfaces using MCP (Model Context Protocol)
• Build reusable Agent Skills (tools) with clear contracts (JSON Schema).
2. MCP & Protocol-Level Integration
• Implement and manage FastMCP (Python) services.
• Enable communication via:
o SSE (Server-Sent Events) transport
o Structured tool contracts
• Ensure scalable and secure agent-tool interaction layers.
3. LLM Integration & Optimization
• Work with:
o Azure OpenAI
o Azure AI Foundry
• Implement:
o Prompt engineering and versioning
o Multi-model routing strategies
• Optimize for:
o Latency
o Cost (token usage)
o Output quality
4. RAG & Knowledge Systems
• Design advanced RAG pipelines using:
o Azure AI Search (hybrid vector + keyword retrieval)
o Agentic retrieval strategies
• Handle:
o Document ingestion pipelines
o Embedding strategies
o Context window optimization
• Minimize hallucinations and improve answer grounding.
5. Data & System Integration
• Work with distributed data systems:
o Cosmos DB (AI state/storage)
o Azure Blob Storage
o Redis (caching)
o AWS RDS + S3 (existing systems)
• Integrate AI workflows with enterprise APIs and legacy systems.
6. AI Workflow Orchestration
• Build orchestration for:
o Multi-step reasoning workflows
o Long-running agent tasks
• Ensure:
o Fault tolerance
o Retry mechanisms
o State management across workflows
7. Evaluation & AI Quality Engineering (Critical)
• Design evaluation frameworks for:
o LLM outputs
o Agent workflows
• Implement:
o Automated evaluation pipelines
o Golden datasets and scoring systems
• Track:
o Accuracy, relevance, consistency
8. Observability & AIOps
• Implement deep observability using:
o Azure AI Foundry Observability (agent tracing)
o Azure Monitor, App Insights, Log Analytics
o Custom audit hooks (MCP level)
• Track:
o Agent decisions
o Latency and failures
o Token usage and cost
• Enable AI-driven monitoring and anomaly detection.
9. Security, Governance & Compliance
• Implement:
o Secure tool access and agent permissions
o Data privacy controls
• Work with:
o Microsoft Entra ID (OAuth2, SSO)
• Ensure auditability of AI decisions and actions.
10. Performance & Cost Optimization
• Optimize:
o LLM usage (caching, batching, routing)
o Retrieval efficiency
• Implement cost governance across:
o Azure AI services
o AWS compute/storage
11. Cross-Cloud & Platform Collaboration
• Work across:
o Azure (AI + core platform)
o AWS (existing infra dependencies)
• Collaborate with:
o Enterprise Architect
o Solution Architect
o Full-stack developers
o DevOps engineers
Required Skills & Experience
Core Experience
• 6+ years in software engineering.
• 2+ years of hands-on experience in LLM/AI systems.
• Proven experience building production-grade AI applications.
Programming & Systems
• Strong expertise in Python (mandatory).
• Experience integrating with .NET-based systems.
• Strong understanding of:
o APIs
o Microservices
o Event-driven systems
AI/LLM Expertise
• Hands-on with:
o Azure OpenAI / OpenAI APIs
o Prompt engineering and tuning
• Strong understanding of:
o Embeddings, tokenization, context windows
o RAG architectures
Agentic AI & MCP (Highly Preferred)
• Experience building:
o Multi-agent systems
o Tool-using agents
• Familiarity with:
o MCP (Model Context Protocol)
o FastMCP or similar frameworks
Azure AI Ecosystem
• Azure AI Foundry
• Azure AI Agent Service
• Azure AI Search
• Azure Observability stack
Data & Infrastructure Awareness
• Experience with:
o Cosmos DB, Redis
o Blob Storage
o AWS (EC2, S3, RDS basics)
Nice to Have (High Impact Differentiators)
• Basic to intermediate experience in Machine Learning, including:
o Classification, regression, and clustering techniques
o Feature engineering and data preprocessing
o Model evaluation metrics (precision, recall, F1, etc.)
• Experience with ML libraries such as:
o Scikit-learn, XGBoost, LightGBM
• Understanding of when to use:
o ML models vs LLMs vs rule-based systems
• Exposure to MLOps practices (model versioning, monitoring, retraining)
• Experience with real-time or batch ML inference systems
• Exposure to DAM systems (e.g., Adobe DAM)
• Knowledge of real-time streaming systems (SSE/WebSockets)
• Experience using GitHub Copilot for development workflows
Soft Skills
• Strong system thinking and architectural mindset
• Clear communication across business and engineering teams
• High ownership and accountability
Location: Bangalore
Exp: 6+ Years
Job Description:
Agentic AI Architecture & Development
• Design and implement multi-agent systems using:
o Azure AI Agent Service
o Microsoft Agentic Framework
• Define:
o Agent roles, memory, and orchestration patterns
o Tool/skill interfaces using MCP (Model Context Protocol)
• Build reusable Agent Skills (tools) with clear contracts (JSON Schema).
2. MCP & Protocol-Level Integration
• Implement and manage FastMCP (Python) services.
• Enable communication via:
o SSE (Server-Sent Events) transport
o Structured tool contracts
• Ensure scalable and secure agent-tool interaction layers.
3. LLM Integration & Optimization
• Work with:
o Azure OpenAI
o Azure AI Foundry
• Implement:
o Prompt engineering and versioning
o Multi-model routing strategies
• Optimize for:
o Latency
o Cost (token usage)
o Output quality
4. RAG & Knowledge Systems
• Design advanced RAG pipelines using:
o Azure AI Search (hybrid vector + keyword retrieval)
o Agentic retrieval strategies
• Handle:
o Document ingestion pipelines
o Embedding strategies
o Context window optimization
• Minimize hallucinations and improve answer grounding.
5. Data & System Integration
• Work with distributed data systems:
o Cosmos DB (AI state/storage)
o Azure Blob Storage
o Redis (caching)
o AWS RDS + S3 (existing systems)
• Integrate AI workflows with enterprise APIs and legacy systems.
6. AI Workflow Orchestration
• Build orchestration for:
o Multi-step reasoning workflows
o Long-running agent tasks
• Ensure:
o Fault tolerance
o Retry mechanisms
o State management across workflows
7. Evaluation & AI Quality Engineering (Critical)
• Design evaluation frameworks for:
o LLM outputs
o Agent workflows
• Implement:
o Automated evaluation pipelines
o Golden datasets and scoring systems
• Track:
o Accuracy, relevance, consistency
8. Observability & AIOps
• Implement deep observability using:
o Azure AI Foundry Observability (agent tracing)
o Azure Monitor, App Insights, Log Analytics
o Custom audit hooks (MCP level)
• Track:
o Agent decisions
o Latency and failures
o Token usage and cost
• Enable AI-driven monitoring and anomaly detection.
9. Security, Governance & Compliance
• Implement:
o Secure tool access and agent permissions
o Data privacy controls
• Work with:
o Microsoft Entra ID (OAuth2, SSO)
• Ensure auditability of AI decisions and actions.
10. Performance & Cost Optimization
• Optimize:
o LLM usage (caching, batching, routing)
o Retrieval efficiency
• Implement cost governance across:
o Azure AI services
o AWS compute/storage
11. Cross-Cloud & Platform Collaboration
• Work across:
o Azure (AI + core platform)
o AWS (existing infra dependencies)
• Collaborate with:
o Enterprise Architect
o Solution Architect
o Full-stack developers
o DevOps engineers
Required Skills & Experience
Core Experience
• 6+ years in software engineering.
• 2+ years of hands-on experience in LLM/AI systems.
• Proven experience building production-grade AI applications.
Programming & Systems
• Strong expertise in Python (mandatory).
• Experience integrating with .NET-based systems.
• Strong understanding of:
o APIs
o Microservices
o Event-driven systems
AI/LLM Expertise
• Hands-on with:
o Azure OpenAI / OpenAI APIs
o Prompt engineering and tuning
• Strong understanding of:
o Embeddings, tokenization, context windows
o RAG architectures
Agentic AI & MCP (Highly Preferred)
• Experience building:
o Multi-agent systems
o Tool-using agents
• Familiarity with:
o MCP (Model Context Protocol)
o FastMCP or similar frameworks
Azure AI Ecosystem
• Azure AI Foundry
• Azure AI Agent Service
• Azure AI Search
• Azure Observability stack
Data & Infrastructure Awareness
• Experience with:
o Cosmos DB, Redis
o Blob Storage
o AWS (EC2, S3, RDS basics)
Nice to Have (High Impact Differentiators)
• Basic to intermediate experience in Machine Learning, including:
o Classification, regression, and clustering techniques
o Feature engineering and data preprocessing
o Model evaluation metrics (precision, recall, F1, etc.)
• Experience with ML libraries such as:
o Scikit-learn, XGBoost, LightGBM
• Understanding of when to use:
o ML models vs LLMs vs rule-based systems
• Exposure to MLOps practices (model versioning, monitoring, retraining)
• Experience with real-time or batch ML inference systems
• Exposure to DAM systems (e.g., Adobe DAM)
• Knowledge of real-time streaming systems (SSE/WebSockets)
• Experience using GitHub Copilot for development workflows
Soft Skills
• Strong system thinking and architectural mindset
• Clear communication across business and engineering teams
• High ownership and accountability
