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/GCP).
- 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/GCP) for AI deployment.
- Strong understanding of REST APIs, microservices, and integration patterns.
- Experience with Git, CI/CD, Docker, and model deployment best practices.
Skills:- Generative AI (GenAI), Agentic AI, Large Language Models (LLM), Retrieval Augmented Generation (RAG) and Python
