Job Description
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
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