Nisum

Machine Learning & Generative AI Engineer (2-5 years of experience only required

Nisum, San Jose

Machine Learning & Generative AI Engineer (2-5 years of experience only required)

Pay found in job post.

Base pay range: $30.00/hr - $40.00/hr

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.

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.

$30 - $40 per hour

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