Gen AI

Job Title: GenAI Engineer (511 Years) – Immediate Joiners Preferred

Experience: 5 – 11 Years

Employment Type: Full-Time

Job Summary

We are seeking an experienced GenAI Engineer with strong expertise in Generative AI, Agent Frameworks, Agentic AI, RAG (Retrieval-Augmented Generation), and MCP (Model Context Protocol). The ideal candidate will design and implement intelligent, scalable AI systems leveraging LLMs, multi-agent workflows, and contextual reasoning.

Candidates who can join immediately or within short notice periods are highly preferred.

Key Responsibilities

• Design and develop GenAI-powered applications using modern LLM ecosystems
• Build and optimize RAG pipelines for context-aware AI solutions
• Develop Agentic AI systems using frameworks like LangChain, AutoGen, CrewAI, or similar
• Implement and manage multi-agent orchestration and workflows
• Utilize Model Context Protocol (MCP) for advanced prompt and context management
• Integrate LLMs with enterprise systems, APIs, and databases
• Fine-tune prompts and evaluate model performance (accuracy, latency, cost)
• Ensure security, scalability, and reliability of AI solutions
• Collaborate with cross-functional teams including data engineering, product, and DevOps
• Stay updated on emerging GenAI tools, frameworks, and best practices

Required Skills & Qualifications

• Strong experience in Generative AI / LLM-based application development
• Hands-on experience with RAG architectures (vector DBs like FAISS, Pinecone, Weaviate, etc.)
• Expertise in Agent Frameworks (LangChain, AutoGen, CrewAI, etc.)
• Experience building Agentic AI systems / multi-agent orchestration
• Knowledge of MCP (Model Context Protocol) or equivalent context-management approaches
• Strong programming skills in Python
• Experience working with OpenAI, Azure OpenAI, or similar LLM APIs
• Familiarity with prompt engineering and evaluation techniques
• Experience with REST APIs, microservices architecture

Preferred Qualifications

• Experience with fine-tuning / embedding models
• Familiarity with vector databases and semantic search
• Exposure to cloud platforms (AWS / Azure / GCP)
• Knowledge of ML Ops / LLM Ops tools
• Experience with streaming pipelines and real-time AI systems
• Understanding of data privacy and AI governance