Senior Data Scientist ML + GenAI / Agentic AI

Senior Data Scientist – ML, GenAI & Agentic AI

Location: Santa Clara, CA
Experience: 5–7 years relevant experience
Employment Type: Full-Time

About the Role:

This is not a research-only or GenAI exploration role. We are looking for an experienced practitioner who has built and deployed AI/ML solutions in production, can work closely with product and engineering teams, and can confidently communicate technical concepts and architecture decisions to customers and non-technical stakeholders.

Experience in the energy domain is highly preferred.

Key Responsibilities

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  Design, develop, and deploy machine learning models for complex business problems.

  
  


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  Apply traditional ML techniques including classification, regression, forecasting, anomaly detection, feature engineering, and model evaluation.

  
  


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  Build and productionize LLM and Generative AI solutions.

  
  


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  Develop Agentic AI workflows using technologies such as LangGraph, LangChain, MCP, tool calling, and agent orchestration.

  
  


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  Design and implement production-grade RAG solutions, including vector databases, hybrid retrieval, reranking, and knowledge graphs.

  
  


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  Work with AWS Bedrock or comparable enterprise GenAI cloud platforms.

  
  


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  Analyze large and complex datasets using Python, SQL, and modern data platforms.

  
  


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  Collaborate with product managers, engineers, customers, and cross-functional stakeholders.

  
  


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  Communicate architecture decisions, model performance, trade-offs, and recommendations to both technical and non-technical audiences.

  
  


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  Support production deployment, monitoring, evaluation, and continuous improvement of AI/ML systems.

  
  


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  Mentor junior data scientists and contribute to technical best practices.

  
  

Required Skills

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  5–7 years of relevant Data Science / Machine Learning experience.

  
  


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  Strong hands-on traditional ML/Data Science background.

  
  


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  Strong Python and SQL skills.

  
  


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  Experience with ML frameworks such as Scikit-learn, XGBoost, CatBoost, TensorFlow, or PyTorch.

  
  


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  12+ months of recent, continuous hands-on GenAI/LLM experience.

  
  


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  Proven production deployment of AI/ML or GenAI systems.

  
  


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  Hands-on Agentic AI production experience, including one or more of:

  
  
  
  
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    LangGraph

    
    
  
  
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    LangChain

    
    
  
  
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    MCP

    
    
  
  
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    Tool calling

    
    
  
  
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    Agent orchestration

    
    
  
  
  
  


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  Strong RAG implementation experience, including:

  
  
  
  
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    Vector databases

    
    
  
  
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    Hybrid retrieval

    
    
  
  
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    Reranking

    
    
  
  
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    Knowledge graphs

    
    
  
  
  
  


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  AWS Bedrock experience strongly preferred.

  
  


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  Azure OpenAI or Google Vertex AI may be considered as alternate GenAI cloud experience.

  
  


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  Experience with big data technologies such as Spark/Hadoop.

  
  


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  Experience with Databricks and/or Snowflake is preferred.

  
  


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  Experience with LLMOps, evaluation, monitoring, or AI evaluation tooling is a plus.

  
  


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  Strong communication and client-facing/consulting skills.

  
  

Preferred Domain Experience

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  Energy / Utilities / Oil & Gas

  
  


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  Enterprise technology

  
  


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  Consulting

  
  


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  Large-scale enterprise products