GenAI Developer Agentic AI & LLM Platforms

Job Title: GenAI Developer Agentic AI & LLM Platforms

Location: Chennai

Experience: 5+ years (with 2+ years in GenAI/LLM ecosystems)

Role Overview

We are looking for a hands-on GenAI Developer to design, build, test, and deploy Generative AI applications using large language models, retrieval-augmented generation, semantic search, vector databases, prompt engineering, FastAPI-based services, and agentic frameworks. The role will contribute to enterprise chatbots, knowledge assistants, document intelligence, automation agents, code assistants, and AI-led business process automation.

Key Responsibilities

GenAI Application Development

• Develop GenAI applications using LLM APIs, prompt templates, structured outputs, RAG pipelines, embeddings, vector search, and tool calling.
• Build conversational AI, enterprise search, document Q&A, summarization, classification, data extraction, and workflow automation use cases.
• Integrate GenAI features with backend applications, portals, APIs, databases, document stores, and enterprise systems.
• Implement prompt orchestration, context management, response formatting, source citation handling, and feedback capture.

RAG, Semantic Search & Knowledge Ingestion

• Build document ingestion pipelines including parsing, OCR coordination, chunking, metadata extraction, embedding generation, indexing, and refresh.
• Implement semantic search and hybrid search using vector databases and search platforms.
• Support retrieval optimization through metadata filtering, chunking strategy, re-ranking, grounding, and citation generation.
• Work with enterprise knowledge sources such as SharePoint, Confluence, Google Drive, S3, databases, CRM, ITSM tools, and document repositories.

Agentic AI Development

• Develop basic to intermediate agentic workflows using LangChain, LangGraph, CrewAI, LlamaIndex, or equivalent frameworks.
• Implement agents with tools, memory, reasoning steps, API actions, and human-in-the-loop checkpoints.
• Build semi-autonomous workflows for business process automation while following safety and approval guardrails.

Backend, API & Data Engineering

• Develop backend services using Python, FastAPI, Node.js, or similar technologies.
• Build REST APIs for LLM orchestration, retrieval, ingestion, evaluation, semantic search, and agent execution.
• Work with SQL and NoSQL databases such as PostgreSQL, MySQL, SQL Server, MongoDB, Cosmos DB, DynamoDB, or equivalent.
• Use Redis or equivalent caching for session state, conversational memory, frequently accessed retrieval results, rate limiting, and workflow state.
• Follow secure coding, logging, exception handling, request validation, OpenAPI documentation, and production deployment practices.

Testing, Evaluation & Observability

• Support prompt testing, LLM response validation, RAG evaluation, regression testing, and hallucination checks.
• Use tools such as LangSmith, Langfuse, RAGAS, DeepEval, TruLens, Promptfoo, or equivalent under guidance.
• Capture logs, traces, token usage, latency, cost, feedback, and quality signals for GenAI applications.

Good to Have

• Exposure to Kafka or messaging systems for asynchronous processing.
• Exposure to MCP server development, tool integration patterns, or agent-to-tool communication.
• Basic understanding of A2A communication patterns and multi-agent orchestration.
• Familiarity with Docker, CI/CD, GitHub Actions, Azure DevOps, Jenkins, or equivalent.
• Exposure to fine-tuning, SLMs, vLLM, Ollama, or Hugging Face model deployment.
• Frontend integration awareness using React, Angular, Streamlit, Gradio, or similar.
• Domain exposure in banking, healthcare, insurance, retail, telecom, or enterprise operations.

Soft Skills

• Strong problem-solving and analytical thinking.
• Ability to work in agile delivery teams and collaborate with cross-functional stakeholders.
• Good communication and documentation skills.
• Ability to learn and evaluate fast-evolving GenAI tools, frameworks, and patterns.

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Required Skills

• Core GenAI (Hands-on)
  
• LLM APIs
• Prompt Engineering
• Embeddings
• Retrieval-Augmented Generation (RAG)
• Semantic Search
• Vector Databases
• Structured Outputs
• Grounding Techniques
• Frameworks (Hands-on)
  
• LangChain
• LangGraph
• CrewAI
• LlamaIndex
• Langflow
• Backend APIs (Hands-on)
  
• Python
• FastAPI
• Node.js
• REST APIs
• OpenAPI
• Asynchronous Processing
• Data Stores (Working Knowledge)
  
• SQL Databases
• NoSQL Databases
• Vector Databases
• Redis Caching
• Object Storage
• Cloud AI (Working Knowledge)
  
• Azure OpenAI
• AWS Bedrock
• Google Vertex AI
• OpenAI
• Anthropic Claude
• Google Gemini
• Hugging Face
• Document Processing (Working Knowledge)
  
• PDF Processing
• Word Document Processing
• Excel Processing
• HTML Processing
• OCR (Optical Character Recognition)
• Document Chunking
• Metadata Extraction
• Indexing Pipelines
• Evaluation & Observability (Basic to Working Knowledge)
  
• LangSmith
• Langfuse
• RAGAS
• Promptfoo
• OpenTelemetry Fundamentals
• Security & Responsible AI (Basic Knowledge)
  
• PII Handling and Data Protection
• Access Control and Authorization
• Prompt Injection Prevention
• Content Filtering
• Audit Logging
• Responsible AI Practices and Governance