Role Summary:

We are seeking a skilled GenAI & Agentic AI Engineer with strong experience in building end-to-end AI/ML solutions, Generative AI applications, and agent-based automation workflows. The ideal candidate will have a solid background in machine learning along with hands-on 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 embedding-based retrieval systems.

- Build scalable AI services using Python, FastAPI/Flask, and cloud platforms (Azure/AWS/GCP).

- Collaborate with cross-functional teams to define use cases and convert them into production-ready GenAI solutions.

- Implement hallucination reduction, prompt-engineering 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 hands-on experience in Generative AI (LLMs, embeddings, RAG, LLM-based apps).

- 6+ months of hands-on experience with Agentic AI frameworks (CrewAI / AutoGen / LangGraph / LangChain Agents).

- Strong proficiency in Python and ML libraries (Scikit-learn, Pandas, NumPy).

- Experience with OpenAI APIs, Azure OpenAI, HuggingFace, and prompt engineering.

- Familiarity with building scalable APIs using FastAPI, Flask, or Django.

- Hands-on 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.