Job Summary
To designs, build, and deploy production-grade enterprise applications powered by large language models, retrieval-augmented generation (RAG) pipelines, and autonomous AI agents.
Key Responsibilities
Gen AI Architecture: Design and implement RAG systems, multi-agent workflows, and orchestration frameworks.
LLM Integration: Integrate foundation models (GPT, LLaMA, Claude) with enterprise data lakes, microservices, and external APIs.
Optimization & MLOps: Fine-tune models, monitor inference latency, manage vector databases, and control production costs.
Collaboration & Mentorship: Work with cross-functional product and data science teams while guiding junior developers
Skill Requirements
10-12+ years of Technical Experience in RPA, GenAI and allied skills.
Programming: Advanced proficiency in Python and backend web frameworks (FastAPI, Flask).
AI Frameworks: Deep hands-on experience with LangChain, LlamaIndex, and vector databases (Pinecone, Chroma, FAISS).
Cloud & Infrastructure: Containerization experience using Docker, Kubernetes, and major cloud platforms (AWS, Azure, or GCP).
Other Requirements
Core Programming & ArchitectureAdvanced Python: Mastery of Python and backend frameworks like FastAPI or Flask to build robust AI services.
Distributed Computing: Experience with PySpark or similar tools for large-scale data processing pipelines.
System Design: Ability to design microservices, event-driven architectures, and decoupled, scalable components
Gen AI & LLM ExpertiseModel Orchestration: Working with major foundation models (GPT, Claude, Gemini, LLaMA) via native SDKs and APIs.
RAG Systems: Designing Retrieval-Augmented Generation pipelines, context engineering, and vector databases (Pinecone, Weaviate, Chroma).
Agentic Workflows: Building autonomous multi-agent systems and tool-calling architectures using frameworks like LangChain, LangGraph, LlamaIndex, or CrewAI.
Fine-Tuning & Evaluation: Implementing PEFT, LoRA, and QLoRA, alongside evaluation frameworks like Ragas or DeepEval to measure hallucination and model quality.
MLOps & Cloud InfrastructureCloud Platforms: Hands-on deployment using hyperscalers like AWS (Bedrock), Microsoft Azure (Azure OpenAI), or Google Cloud (Vertex AI).
Containerization & CI/CD: Packaging applications with Docker and Kubernetes, and managing automated deployment pipelines.
Observability: Tracking latency, token costs, LLM tracing, and output performance in production environments.