AI ML engineer - 23454

Sumeru Solutions

  • Woodland Hills, CA
  • 1 day ago

    Highlights

    Build document extraction, parsing, and chunking pipelines for structured and unstructured data Train, evaluate, and fine-tune ML models; manage tagging and labeling workflows Implement embedding generation and vector search solutions Integrate ML models with Vector DBs and MongoDB Ensure code quality, scalability, and production readiness Required Qualifications Expert-level proficiency in Python Strong experience in model training, evaluation, and tagging workflows Hands-on experience with document extraction and chunking techniques Solid understanding of ML algorithms and Generative AI concepts Experience working with Vector Databases and/or MongoDB. The ideal candidate will have deep expertise in Python, hands-on experience in model training, document processing pipelines, and strong knowledge of vector databases and modern ML/GenAI frameworks.

    Numbers & Facts

    LocationWoodland Hills, CA

    Description

    Role: AI ML engineer

    Woodland Hills, CA

    Mandatory Skills;

    • 8+ years of Python development experience
    • 5+ years of Machine Learning model development and training
    • 3+ years of Generative AI / LLM solution development
    • 3+ years of Vector Database, Embeddings, and RAG implementation
    • 2+ years of A2A (Agent-to-Agent) and MCP (Model Context Protocol) implementation experience
    • 2+ years of AI Agent and Multi-Agent System development experience

    Job Title: Lead II - ML Engineering Data Science Engineer

    Role Overview We are seeking a highly skilled Data Science Engineer to design and develop scalable ML and Generative AI solutions. The ideal candidate will have deep expertise in Python, hands-on experience in model training, document processing pipelines, and strong knowledge of vector databases and modern ML/GenAI frameworks.

    Key Responsibilities

    Develop and deploy machine learning and GenAI solutions using Python Design and optimize prompt engineering strategies for LLM-based applications

    Build document extraction, parsing, and chunking pipelines for structured and unstructured data Train, evaluate, and fine-tune ML models; manage tagging and labeling workflows Implement embedding generation and vector search solutions Integrate ML models with Vector DBs and MongoDB Ensure code quality, scalability, and production readiness Required Qualifications Expert-level proficiency in Python Strong experience in model training, evaluation, and tagging workflows Hands-on experience with document extraction and chunking techniques Solid understanding of ML algorithms and Generative AI concepts Experience working with Vector Databases and/or MongoDB

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