AI Developer

Greetings from TATA CONSULTANCY SERVICES!

Skill: Python, Azure AI Foundry/OpenAI + LangChain/LangGraph

Years of Experience: 5-10 years

Location: Chennai, Bangalore, Pune

Job Description:-

• 5–8+ years of professional software engineering experience
• Strong hands‑on expertise in Python for backend and AI application development
• Practical experience building LLM‑based applications using Azure AI Foundry and Azure OpenAI Service
• Hands‑on experience implementing LangChain and LangGraph workflows
• Strong understanding of LLMs, prompt engineering, RAG architectures, and NLP fundamentals
• Experience working with Azure AI Search, vector databases, embeddings, and hybrid search techniques
• Strong knowledge of RESTful APIs, microservices, and event‑driven architectures
• Experience developing and deploying cloud‑native applications on Microsoft Azure
• Familiarity with CI/CD pipelines, DevOps practices, and cloud monitoring tools
• Understanding of AI safety concepts including content filtering, guardrails, and hallucination mitigation
• Experience building evaluation and regression testing mechanisms for AI workflows
• Strong problem‑solving skills and ability to work in fast‑paced, collaborative environments
• Good communication skills and ability to work with cross‑functional teams

Key Responsibilities:

Design, build, and deploy Generative AI and LLM‑powered applications using Azure AI Foundry and Azure OpenAI

• Develop AI use cases such as conversational AI, copilots, knowledge assistants, and document intelligence solutions

• Integrate AI capabilities into enterprise systems using APIs and microservices

• Implement LLM orchestration logic using LangChain chains, tools, retrievers, and memory components

• Build stateful and graph‑based workflows using LangGraph for multi‑step reasoning and agentic patterns

• Develop multi‑agent, supervisor‑worker, and tool‑calling workflows with effective context and memory management

• Build and optimize Retrieval Augmented Generation (RAG) pipelines using Azure AI Search and embedding models

• Engineer NLP pipelines for text extraction, classification, summarization, entity recognition, and semantic search

• Curate, chunk, embed, and validate enterprise knowledge sources to ensure accuracy and freshness

• Develop production‑grade Python services and libraries for AI workloads

• Implement API layers, orchestration services, async execution, streaming, and batching

• Optimize AI applications for latency, cost, token usage, and prompt efficiency

• Follow engineering best practices for modularity, testing, version control, and reuse

• Implement AI safety guardrails including content moderation, prompt validation, and response checks

• Build evaluation and feedback loops for accuracy, relevance, hallucination detection, and regression testing

• Ensure AI solutions comply with enterprise security, privacy, and data‑handling standards

• Deploy AI workloads using Azure App Service, Azure Functions, or AKS

• Integrate AI applications with Azure Entra ID for authentication and authorization

• Enable monitoring, logging, tracing, and observability for AI workflows in production