Sr GenAI Engineer

Job Summary

We are seeking an experienced Senior GenAI Engineer to design, develop, and deploy enterprise-grade Generative AI solutions. The ideal candidate will have strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, prompt engineering, vector databases, and cloud-based AI platforms. The role involves building scalable AI applications, mentoring team members, and collaborating with stakeholders to deliver innovative AI-powered solutions.

Key Responsibilities

• Design and develop GenAI applications using LLMs and multimodal AI models.
• Build and optimize RAG pipelines using vector databases and semantic search.
• Develop Agentic AI systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, and LlamaIndex.
• Implement prompt engineering strategies and fine-tune foundation models for business use cases.
• Integrate AI solutions with enterprise systems, APIs, and cloud services.
• Develop scalable AI services and deploy them using MLOps and CI/CD practices.
• Configure AI guardrails, content moderation, and PII protection mechanisms.
• Monitor model performance, evaluation metrics, hallucination rates, and system observability.
• Collaborate with product managers, architects, and business stakeholders to define AI solutions.
• Mentor junior engineers and provide technical leadership across AI initiatives.
• Stay updated with emerging trends in Generative AI, LLMOps, and Agentic AI technologies.

Required Skills

Technical Skills

• Strong proficiency in Python.
• Experience with LLMs such as GPT, Claude, Llama, Mistral, and Gemini.
• Expertise in RAG, embeddings, vector search, and knowledge retrieval systems.
• Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or Redis.
• Strong understanding of NLP, Machine Learning, Deep Learning, and Transformer architectures.
• Knowledge of cloud platforms: Azure, AWS, or Google Cloud Platform.
• Experience with MLOps, CI/CD, Docker, Kubernetes, and model deployment.
• Familiarity with SQL and NoSQL databases.
• Knowledge of AI safety, guardrails, governance, and responsible AI practices.