Azure AI Engineer

TEKsystems Global Services in India; India; On-site; INR 4,500,000 - 7,500,000; Full time

Job summary

TEKsystems Global Services in India is seeking an experienced Azure AI/ML Engineer to design, develop, deploy, and support production-grade AI solutions using Azure AI services and modern orchestration frameworks.

The role focuses on building agentic and generative AI, RAG pipelines, agent orchestration, and observability in cloud environments, with emphasis on scalable, production-ready architectures.

Qualifications

• 8+ years in software engineering, AI/ML, or data engineering.
• 4+ years building GenAI, Agentic AI, or RAG-based solutions.
• Experience deploying and supporting production AI applications.

Responsibilities

• Design, build, and deploy Agentic AI and Generative AI solutions using Azure AI technologies.
• Develop and optimize RAG pipelines for enterprise use cases.
• Build and manage multi-agent architectures and orchestration workflows.
• Implement prompt engineering, tool calling, MCP integrations, and A2A communication.
• Design retrieval, embedding, grounding strategies for LLM apps.
• Deploy and monitor AI solutions in production with telemetry.

Skills

Azure AI/ML

Generative AI

LLMs

Agentic AI

RAG pipelines

Multi-Agent Systems

Python programming

REST APIs

Azure cloud services

Tools

LangChain

LangGraph

Semantic Kernel

AutoGen

CrewAI

DeepAgents

MCP Frameworks

Job description

We are seeking an experienced Azure AI/ML Engineer with strong expertise in Generative AI, Agentic AI, LLMs, RAG, and Multi-Agent Systems. The ideal candidate will be responsible for designing, developing, deploying, and supporting production-grade AI solutions using Azure AI services, modern orchestration frameworks, and enterprise AI engineering practices.

This role requires hands‑on experience building intelligent agents, agent orchestration frameworks, retrieval-augmented generation (RAG) solutions, observability frameworks, and scalable AI applications in cloud environments.

Key Responsibilities

• Design, build, and deploy Agentic AI and Generative AI solutions using Azure AI technologies.
• Develop and optimize RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases.
• Build and manage multi-agent architectures, orchestration workflows, and agent collaboration patterns.
• Implement prompt engineering, tool calling, MCP integrations, and agent-to-agent (A2A) communication.
• Design retrieval, chunking, embedding, reranking, and grounding strategies for LLM applications.
• Deploy and maintain AI solutions in production environments with monitoring, telemetry, and observability capabilities.
• Evaluate model performance using quantitative and qualitative metrics.
• Collaborate with architects, product teams, and business stakeholders to deliver AI-powered solutions.

Required Technical Skills

Generative AI & LLMs

• Strong hands‑on experience with:
• Prompt Engineering
• AI Agent Development

Agentic AI

• Experience designing and implementing:
• Multi-Agent Systems
• Agent Orchestration
• Agent Routing & Intent Classification
• Agent-to-Agent (A2A) Communication
• Agent Skills and Tool Integrations

RAG & Knowledge Retrieval

• Strong understanding of:
• Embeddings & Vector Search
• Retrieval and Re-ranking
• Grounded Responses
• Knowledge Base Integration

AI Frameworks

Hands‑on experience with one or more:

• LangChain
• LangGraph
• Semantic Kernel
• AutoGen
• CrewAI
• DeepAgents
• MCP Frameworks

AI Evaluation & Monitoring

Experience with:

• Context Precision & Recall
• Groundedness
• Correctness & Completeness Metrics
• Response Quality Evaluation
• Telemetry & Observability
• Production Monitoring and Alerting

Programming & Cloud

• Advanced Python development skills.
• REST APIs and backend development.
• Experience with Azure cloud services and AI workloads.

Preferred Skills

• Vector Databases
• Knowledge Graphs (Neo4j)
• Conversational AI and Voice Agents
• MLOps and Model Lifecycle Management

Experience

• 8+ years of software engineering, AI/ML, or data engineering experience.
• 4+ years of hands‑on experience building GenAI, Agentic AI, or RAG-based solutions.
• Experience deploying and supporting production AI applications.

Ideal Candidate Profile

Candidates should demonstrate strong practical expertise in:

• Building Agentic AI solutions and Multi-Agent Architectures
• Designing and optimizing RAG pipelines
• Agent orchestration, routing, and workflow design
• MCP and tool integration patterns
• LLM evaluation, benchmarking, and monitoring
• Production support, observability, and operational excellence
• Architecture design and technical decision-making

Preferred Domain Experience: Enterprise AI Assistants, Conversational AI, Intelligent Automation, Knowledge Management, Customer Service AI, or Digital Transformation initiatives.