Huron is seeking a Machine Learning Engineer to design, build, and deploy intelligent systems that solve complex business problems across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries.
Requirements
- 2+ (3+ years for Sr. Associate) years of hands-on experience building and deploying ML solutions in production—not just notebooks and prototypes.
- Strong Python and JavaScript programming skills with deep experience in the ML ecosystem (NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, etc.) and proficiency with JavaScript web app development.
- Solid foundation in ML fundamentals: supervised and unsupervised learning, model evaluation, feature engineering, hyperparameter tuning, and understanding of when different approaches are appropriate.
- Experience with cloud ML platforms, particularly Azure Machine Learning, with working knowledge of AWS SageMaker or Google AI Platform.
- Proficiency with data platforms: SQL, Snowflake, Databricks, or similar.
- Experience with LLMs and generative AI: prompt engineering, fine-tuning, embeddings, RAG systems, or agent frameworks.
Responsibilities
- Design and build end-to-end ML solutions—from data pipelines and feature engineering through model training, evaluation, and production deployment.
- Develop both traditional ML and generative AI systems, including supervised/unsupervised learning, time-series forecasting, NLP, LLM applications, RAG architectures, and agent-based systems using frameworks like Agent Framework, LangChain, LangGraph, or similar.
- Build financial and operational models that drive business decisions—demand forecasting, pricing optimization, risk scoring, anomaly detection, and process automation for commercial enterprises.
- Create production-grade APIs and services (FastAPI, Flask, or similar) that integrate ML capabilities into client systems and workflows.
- Implement MLOps practices—CI/CD pipelines, model versioning, monitoring, drift detection, and automated retraining to ensure solutions remain reliable in production.
- Collaborate directly with clients to understand business problems, translate requirements into technical solutions, and communicate results to both technical and executive audiences.
Other
- Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, or related quantitative field (or equivalent practical experience).
- Willingness to travel approximately 30% to client sites as needed.
- Ability to communicate technical concepts to non-technical stakeholders and work effectively with cross-functional teams.
- Master's degree or PhD in a quantitative field (preferred).
- Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces (preferred)
