Senior ML Engineer (Client Solutions)
Careers Azx Io · United States
About this role
## About AZX AZX accelerates positive impact in critical industries through AI transformation. We specialize in physics-informed ML and enterprise AI solutions that address climate and sustainability challenges. We work with category leaders across real estate (CBRE), energy (LevelTen Energy), logistics (Flexe), and utilities. We’re growing quickly, bootstrapped profitably in our first year, and are now backed by leading investors focused on AI, climate, and energy. ## About This Role We’re seeking a **Senior ML Engineer (Client Solutions)** who builds ML systems **directly inside client environments**—starting from the client’s real data across multiple systems and ending with models running on a schedule within their environment. You’ll be the engineering face of AZX to clients, wearing many hats (ML, DevOps/infrastructure, and some front-end/back-end) as part of a small team. ## Responsibilities - Own the **full ML delivery lifecycle**: data discovery & cleaning, modeling, evaluation, deployment into the client environment, scheduling, monitoring, and retraining policy. - Build **forecasting and detection models** that hold up under real-world data issues (late feeds, revised rows, missing labels). - Backtest and evaluate models honestly enough to support real operational decisions; defend precision/recall tradeoffs. - Design systems that distinguish **“no prediction” vs “wrong prediction”** so missing answers read differently than incorrect ones. - Ship enough product to make the model usable (e.g., **FastAPI service**, small **React** surface, scheduled job—whatever fits the client). - Own the **measurement story**: align on baselines/KPIs before deployment, instrument monitoring, and deliver post-deployment readouts with clearly stated attribution limits. - Maintain a client-facing engineering presence and feedback loop into the platform team (discovery, working sessions with IT/data teams, demos, surfacing data shapes and failure modes). ## Core Qualifications - **5+ years** shipping applied ML to production. - Experience with forecasting, detection/classification on time series, survival/reliability modeling, or optimization—plus an evaluation you defended. - Strong data engineering skills: you can 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 strong skepticism of suspiciously good metrics. - Enough software engineering to ship real systems: **Python, SQL, tests, Docker, scheduler, API/app surface, monitoring**; type-strict, tested, reviewable code. - Client-facing capability: run discovery, lead demos, and push back early and plainly with alternatives. - Judgment about when ML is the wrong tool—and willingness to say so. - Practical fluency with the stack: - **Python 3.12+** (pandas/polars/DuckDB, scikit-learn, statsmodels, gradient boosting) - **SQL/Postgres** (TimescaleDB/PostGIS for grid work) - Time-series feature engineering and validation - Comfort building usable surfaces: **FastAPI + enough React/TypeScript**, deployed with **Docker** and basic cloud tooling (Azure/AWS). - Working fluency with **LLMs** for agentic edges (extraction/retrieval). Depth not required, but be honest about experience. - Bachelor’s degree; Master’s is a plus. - Domain experience in Energy, Utilities, Infrastructure, and Commercial Real Estate is a plus. ## Why AZX! - Fast-growing, p
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