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.
- Demonstrated ability to design, implement, and optimize ML/GenAI models from scratch.
Education
- Bachelor’s degree required
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Ai Ai Engineer Machine Learning Deep Learning Mlops Generative Ai Pytorch Tensorflow Data Engineer