Machine Learning Engineer; Manager

Machine Learning Engineer (Manager)

Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation, and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.

We are seeking a Machine Learning Engineering Manager to join the Data Science & Machine Learning team in our Commercial Digital practice. You will lead the design, development, and deployment of intelligent systems that solve complex business problems across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries. This role will own the full ML solution lifecycle—from problem definition through production deployment—while managing and mentoring a team of engineers and data scientists.

• Lead and mentor junior ML engineers and data scientists—provide technical guidance, conduct code reviews, and support professional development. Foster a culture of continuous learning and high‑quality engineering practices within the team.
• Manage complex multi‑workstream ML projects—oversee project planning, resource allocation, and delivery timelines. Ensure projects meet quality standards and client expectations while maintaining technical excellence.
• Design and architect end‑to‑end ML solutions—from data pipelines and feature engineering through model training, evaluation, and production deployment. Make key technical decisions and own the overall solution architecture.
• Lead development of 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 such as Agent Framework, Lang Chain, or Lang Graph.
• Build financial and operational models that drive business decisions—demand forecasting, pricing optimization, risk scoring, anomaly detection, and process automation for commercial enterprises.
• Establish MLOps best practices—define and implement CI/CD pipelines, model versioning, monitoring, drift detection, and automated retraining standards to ensure solutions remain reliable in production.
• Serve as a trusted advisor to clients—build long‑standing partnerships, understand business problems, translate requirements into technical solutions, and communicate results to both technical and executive audiences.
• Contribute to practice development—participate in business development activities, develop reusable assets and methodologies, and help shape the technical direction of Huron's DSML capabilities.

• 5+ years of hands‑on experience building and deploying ML solutions in production—not just notebooks and prototypes. You have trained models, put them into production, and maintained them at scale.
• Experience leading and developing technical teams—including coaching, mentorship, code review, and performance management. Demonstrated ability to build high‑performing teams and develop junior talent.
• Strong Python and JavaScript programming skills with deep experience in the ML ecosystem (Num Py, Pandas, Scikit‑learn, PyTorch or Tensor Flow, 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 Sage Maker or Google AI Platform. We are platform‑flexible but Microsoft‑preferred.
• Proficiency with data platforms: SQL, Snowflake, Databricks, or similar. Comfortable working with large datasets and architecting data pipelines.
• Experience with LLMs and generative AI: prompt engineering, fine‑tuning, embeddings, RAG systems, or agent frameworks. You understand both the capabilities and limitations.
• Excellent communication and client management skills—ability to communicate technical concepts to non‑technical stakeholders, lead client meetings, and build trusted relationships with…