Machine Learning & Generative AI Engineer (2-5 Years of Experience Only Required)

What You’ll Do • We are seeking a Machine Learning & Generative AI Engineer with strong expertise in the Azure ecosystem and Databricks, combined with experience in Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and agentic systems with tool use. • The ideal candidate will be comfortable designing and deploying ML and GenAI systems end-to-end, including classical ML models, deep learning solutions, and modern agent frameworks. • Design, implement, and optimize ML and GenAI pipelines on Azure Databricks. • Build and deploy RAG systems and agentic AI systems with tool use for enterprise applications. • Work with Model Context Protocol (MCP) and AI Development Kit (ADK) to build scalable agentic solutions. • Leverage frameworks such as LangChain, LangGraph, LangSmith, and other popular GenAI ecosystems.Conduct EDA, feature engineering, and NAS experiments to improve model performance. • Build and optimize regression, classification, and forecasting models using Scikit-learn, XGBoost, PyTorch, and TensorFlow. • Utilize GPUs for large-scale model training and inference. • Develop, deploy, and monitor models and agents in production environments with proper serving and observability. • Collaborate with data engineers, product managers, and stakeholders to integrate GenAI and ML solutions into business workflows. What You Know • Strong experience with Azure Databricks and broader Azure cloud ecosystem (Data Lake, Data Factory, Synapse, etc.). • Hands-on expertise in Generative AI (LLMs, RAG, agentic frameworks, tool use). • Experience with MCP and ADK for building GenAI and agent workflows. • Proficiency with LangChain, LangGraph, LangSmith, and other modern frameworks for orchestration and observability. • Solid background in Python, NumPy, Pandas, and ML libraries. • Experience in EDA, feature engineering, time-series forecasting, and NAS. • Strong knowledge of ML model development (regression, classification, forecasting) and deep learning frameworks (PyTorch, TensorFlow). • Familiarity with model serving, MLOps practices, and CI/CD for AI systems. • Experience with GPU-enabled ML/GenAI workflows. • Prior industry experiences deploying RAG systems and agentic AI workflows in production. • Exposure to vector databases, embeddings, and semantic search. • Familiarity with observability tools for GenAI pipelines.Strong problem-solving and communication skills with the ability to thrive in cross-functional teams. • 5+ years in ML/AI roles is preferred. • Junior candidates with strong GenAI/agentic experience and the right mindset are also welcome. Education • Bachelor’s degree required