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.

Valuable 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

📌 GenAI Developer Agentic AI & LLM Platforms (Chennai)
🏢 PwC India
📍 Chennai