GenAI & Agentic AI Engineer

Experience Required:

5+ years in

AI/ML GenAI Experience:

Minimum 2 years (handson)

Agentic AI Experience:

Minimum 6 months (CrewAI / AutoGen / LangGraph / LangChain Agents) Role Summary We are seeking a skilled GenAI & Agentic AI Engineer with strong experience in building endtoend AI/ML solutions, Generative AI applications, and agentbased automation workflows. The ideal candidate will have a solid background in machine learning along with handson expertise in LLMs, RAG, embeddings, vector databases, and Agentic AI frameworks such as CrewAI, AutoGen, LangGraph, or LangChain Agents. Key Responsibilities Build and deploy GenAI applications using LLMs (OpenAI, Azure OpenAI, Claude, Gemini, Llama, etc.). Develop Agentic AI workflows using frameworks such as CrewAI, AutoGen, LangGraph, or LangChain Agents. Design and implement RAG pipelines, vector search solutions, and embeddingbased retrieval systems. Build scalable AI services using Python, FastAPI/Flask, and cloud platforms (Azure/AWS/Google Cloud Platform). Collaborate with crossfunctional teams to define use cases and convert them into productionready GenAI solutions. Implement hallucination reduction, promptengineering strategies, and model evaluation methods. Integrate LLMs with enterprise applications, APIs, and automation workflows. Work with vector databases (FAISS, Pinecone, Chroma, Weaviate) for semantic search. Monitor, evaluate, and optimize GenAI models for accuracy, performance, and cost. Required Skills & Experience 5+ years of experience in AI/ML, including model development, data preprocessing, EDA, training, and evaluation. 2+ years of handson experience in Generative AI (LLMs, embeddings, RAG, LLMbased apps). 6+ months of handson experience with Agentic AI frameworks (CrewAI / AutoGen / LangGraph / LangChain Agents). Strong proficiency in Python and ML libraries (Scikitlearn, Pandas, NumPy). Experience with OpenAI APIs, Azure OpenAI, HuggingFace, and prompt engineering. Familiarity with building scalable APIs using FastAPI, Flask, or Django. Handson knowledge of cloud services (Azure/AWS/Google Cloud Platform) for AI deployment. Strong understanding of REST APIs, microservices, and integration patterns. Experience with Git, CI/CD, Docker, and model deployment best practices.