• Senior ML Engineer

    Insight Global (Eden Prairie, MN)


    Job Description

    Insight Global is seeking a Machine Learning Engineer to design and implement scalable ML solutions on Azure Databricks and build intelligent AI agents that enhance business workflows. This role combines advanced data engineering, model development, and MLOps practices to deliver production‑grade AI systems.

     

    We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

     

    Skills and Requirements

     

    - 5+ years of experience in ML engineering or applied AI roles.

     

    - Strong proficiency with Azure Databricks (PySpark, MLflow, Delta Lake).

     

    - Hands‑on experience building and deploying AI agents or intelligent automation solutions.

     

    - Solid programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit‑learn).

     

    - Experience with Azure Machine Learning, Azure DevOps, and containerization (Docker/Kubernetes).

     

    - Knowledge of data engineering fundamentals and distributed computing.

     

    - Familiarity with LLMOps and techniques for managing large language models in production. - Experience with vector databases, RAG (Retrieval-Augmented Generation), and embeddings for AI agents.

     

    - Exposure to Generative AI platforms (e.g., OpenAI, Hugging Face, Azure OpenAI Service).

     

    - Understanding of Power BI for analytics integration.

     

    - Knowledge of Oracle or other relational databases for hybrid data solutions.