Design and develop Generative AI solutions using LLMs (OpenAI, Azure OpenAI, Hugging Face)
Build and optimize RAG (Retrieval-Augmented Generation) pipelines
Implement frameworks like LangChain, LangGraph, or LlamaIndex
Develop and maintain ML models using Python ecosystem
Work extensively on Azure Databricks for:
Data processing (PySpark)
Model training & experimentation (MLflow)
Deployment & monitoring
Build scalable data pipelines on Databricks Lakehouse (Delta Lake)
Integrate vector databases / search systems (Azure AI Search, Pinecone, FAISS, etc.)
Deploy ML/GenAI solutions using MLOps best practices
Collaborate with cross-functional teams for production-grade AI solutions

Experience with Databricks MosaicML / Vector Search
Knowledge of Docker, Kubernetes
Familiarity with CI/CD pipelines
Experience in building end-to-end AI applications

Strong programming skills in Python
Hands-on experience with:
✅ Generative AI / LLMs
✅ RAG, embeddings, vector search
✅ LangChain / LangGraph / LlamaIndex
Mandatory experience in:
✅ Azure Databricks
✅ PySpark / Spark
Experience with:
Azure OpenAI / Azure AI Studio
MLflow, model serving, MLOps
Understanding of: Transformers, NLP, prompt engineering
Exposure to: REST APIs, FastAPI/Flask (for deployment)

We are looking for a Python ML Engineer with strong expertise in Generative AI and Azure Databricks to design, build, and deploy scalable AI solutions. The ideal candidate should have hands-on experience with LLMs, RAG pipelines, and Azure-based AI ecosystems, along with a solid foundation in machine learning and data engineering.