Senior ML Engineer (Client Solutions)

Core Qualifications

• 5+ years of shipping applied machine learning to production — forecasting, detection/classification on time series, survival/reliability modeling, or optimization — with an evaluation you defended to someone whose job depended on it.
• Strong data engineering skills and willingness to use them: you find, clean, join, and profile data yourself at awkward scale, without a dedicated data team.
• Rigorous validation discipline — chronological splits, walk-forward validation, as-of correctness, and an instinct to be suspicious of a suspiciously good metric.
• Enough software engineering to ship real systems: Python, SQL, tests, Docker, a scheduler, an API or app surface, and monitoring — type-strict, tested, reviewable code, even in a pod of two.
• Client-facing capability and the assertion to use it — running discovery, leading demos, and pushing back early and plainly when an ask is wrong, with an alternative already in hand.
• Judgment about when ML is the wrong tool, and the willingness to say so to a client who wants AI regardless.
• Practical fluency with our core stack — Python 3.12+ (pandas/polars/DuckDB, scikit-learn, statsmodels, gradient boosting), SQL/Postgres (with TimescaleDB/PostGIS for grid work), and time-series feature engineering and validation.
• Comfort building the surfaces that make a model usable — FastAPI plus enough React/TypeScript to expose results — and deploying it with Docker and basic cloud tooling (Azure/AWS).
• Working fluency with LLMs for the agentic edges of client work (extraction, retrieval) — depth isn't required, but honesty about your actual experience is.
• Bachelor's Degree; Master's is a Plus
• Domain experience in Energy, Utilities, Infrastructure, and Commercial Real Estate is a plus

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