Machine Learning & Generative AI Engineer (7+ years of experience only required)

Machine Learning & Generative AI Engineer (7+ years of experience only required)

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'll Do

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 MCP and ADK for scalable agentic solutions.

Leverage frameworks such as LangChain, LangGraph, LangSmith; conduct EDA, feature engineering, and NAS experiments.

Build and optimize regression, classification, and forecasting models with Scikit-learn, XGBoost, PyTorch, TensorFlow.

Utilize GPUs for large-scale training and inference.

Deploy and monitor models and agents with proper serving and observability.

Collaborate with engineering and product teams to integrate GenAI and ML into workflows.

What You Know

Strong experience with Azure Databricks and Azure cloud ecosystem.

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 experience deploying RAG systems and agentic AI workflows in production.

Exposure to vector databases, embeddings, and semantic search.

Strong problem-solving and communication skills; 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

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

IT Services and IT Consulting

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