Data & AI Engineer

We are seeking a highly skilled AI Engineer with strong expertise in customized Large Language Models (LLMs) and hands-on experience with the Databricks platform. The ideal candidate will design, build, fine-tune, and deploy enterprise-grade generative AI solutions , modern AI frameworks, and cloud technologies., * Design, develop, and deploy customized LLM-based applications

• Build scalable Generative AI and RAG (Retrieval-Augmented Generation) solutions on Databricks Lakehouse architecture.
• Fine-tune and optimize open-source and proprietary LLMs using enterprise datasets.
• Develop prompt engineering frameworks and AI orchestration workflows.
• Work with Databricks Mosaic AI, MLflow, Vector Search, and Unity Catalog.
• Build and manage vector databases, embeddings pipelines, and semantic search solutions.
• Integrate AI solutions with enterprise applications, APIs, and cloud platforms.
• Optimize model performance, scalability, inference latency, and cost efficiency.
• Implement AI governance, monitoring, security, and responsible AI practices.
• Collaborate with business stakeholders, data engineers, and product teams to deliver AI solutions.
• Support AI model deployment, MLOps pipelines, and production monitoring.

• Strong hands-on experience with Databricks and Lakehouse architecture.
• Experience with Databricks Mosaic AI, MLflow, Delta Lake, and Unity Catalog.
• Strong programming skills in Python and SQL.
• Hands-on experience with LLMs such as GPT, Llama, Mistral, Claude, or similar models.
• Experience with RAG architectures, embeddings, and vector search implementations.
• Knowledge of LangChain, LangGraph, Semantic Kernel, or similar AI orchestration frameworks.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Familiarity with APIs, Docker, Kubernetes, and microservices architecture.
• Understanding of AI governance, model evaluation, and monitoring.

Preferred Qualifications:

• Experience deploying GenAI applications in enterprise environments.
• Knowledge of distributed computing and Spark optimization.
• Experience with Databricks Model Serving and AI Gateway.
• Familiarity with CI/CD and MLOps practices.
• Experience with multimodal AI models and AI agents.
• Technologies & Tools
• Databricks, Mosaic AI, MLflow, Delta Lake
• Python, SQL, PyTorch, TensorFlow
• OpenAI API, Hugging Face
• LangChain, LangGraph
• Vector databases: Pinecone, FAISS, ChromaDB
• AWS, Azure, Google Cloud Platform
• Docker, Kubernetes, GitHub Actions