Title: Senior AI/ML Engineer

Location: Bangalore

Exp: 6+ Years

:

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).
- 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.
- 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

- 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.
- 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.
- 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

- 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

- 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.
- 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.
- 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

- 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

- Robust system thinking and architectural mindset
- Clear communication across business and engineering teams
- High ownership and accountability

📌 Senior AI/ML Engineer (Bengaluru)
🏢 NR Consulting
📍 Bengaluru