ML Engineer (ML+ Data Bricks)

Job Summary
We are looking for an experienced ML Engineer with strong hands-on expertise in Classical Machine Learning, Generative AI, Agentic AI, and Databricks. The ideal candidate should be capable of designing, developing, deploying, and optimizing end-to-end machine learning and AI solutions in production environments.
Key Responsibilities
• Design and develop end-to-end Classical ML, GenAI, and Agentic AI solutions.
• Build and deploy machine learning models using Python and Databricks.
• Develop ML pipelines covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
• Design and implement Agentic AI workflows, including AI agents, tool/function calling, orchestration, and multi-step reasoning workflows.
• Develop GenAI applications using LLMs, RAG, embeddings, vector search, and prompt engineering.
• Apply Classical ML algorithms for classification, regression, clustering, forecasting, and recommendation use cases.
• Perform feature engineering, model selection, hyperparameter tuning, and model evaluation.
• Implement MLOps practices for model versioning, deployment, monitoring, and lifecycle management.
• Leverage Databricks, MLflow, and related tools to build scalable ML/AI solutions.
• Optimize models and pipelines for performance, scalability, reliability, and cost.
• Collaborate with Data Engineers, Data Scientists, Architects, and business stakeholders to deliver production-ready AI solutions.
Required Skills
Classical ML
• Strong hands-on experience with Scikit-learn
• Experience with XGBoost, LightGBM, and/or CatBoost
• Regression, classification, clustering, feature engineering, and model evaluation
• Experience with model tuning, cross-validation, and handling imbalanced datasets
GenAI
• Hands-on experience with LLMs and Generative AI
• RAG architecture and implementation
• Embeddings and vector databases/search
• Prompt engineering and LLM evaluation
• Experience with LLM APIs and AI application development
Agentic AI
• Experience building AI agents and agentic workflows
• Tool/function calling and workflow orchestration
• Experience with agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar
• Ability to design multi-step AI workflows and integrate external tools/APIs
Databricks & MLOps
• Strong hands-on experience with Databricks
• Experience with MLflow and ML lifecycle management
• Experience building and deploying ML pipelines
• Strong Python and SQL skills
• Understanding of model monitoring, versioning, and CI/CD

Work arrangement
Yes