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.

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

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)

Experience with Databricks MosaicML / Vector Search

Knowledge of Docker, Kubernetes

Familiarity with CI/CD pipelines

Experience in building end-to-end AI applications