> Markdown version of [/jobs/ext/3962809-senior-ml-engineer-client-solutions](https://www.wearedevelopers.com/jobs/ext/3962809-senior-ml-engineer-client-solutions). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior ML Engineer (Client Solutions)

- **Company:** AZX INCORPORATED
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Discovery, Information Engineering, DevOps, Design of User Interfaces, Human-Computer Interaction, Python (Programming Language), PostgreSQL, Machine Learning, PostGIS, Reliability Engineering, Software Engineering, SQL Databases, TypeScript, Feature Engineering, ReactJS, Large Language Models, Fastapi, Pandas, Scikit Learn, Statistics Packages, Xgboost, Docker
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-ml-engineer-client-solutions--b16c742c-a6b3-4307-b2e1-656360aff956

## About the Role

Sustainability, Artificial Intelligence (AI), Physics, Logistics, Real Estate, Economics, Risk, Alternative Energy, DevOps, User Interface/Experience (UI/UX), * 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

## Description

* Own the full ML delivery lifecycle: data discovery and cleaning, modeling, evaluation, deployment into the client environment, scheduling, monitoring, and retraining policy.
 * Build forecasting and detection models that hold up against real-world data quality issues (late feeds, revised rows, missing labels).
 * Backtest and evaluate models honestly enough to stake real operational decisions on them, and defend your precision/recall tradeoffs to the people who bear the cost of false alarms.
 * Design systems that distinguish "no prediction" from "wrong prediction," so a missing answer reads differently to the end user than an incorrect one.
 * Ship enough product to make the model usable - a FastAPI service, a small React surface, a scheduled job - whatever "usable capability" means for that client.
 * Own the measurement story: agree on baselines and KPIs before deployment, instrument for monitoring, and deliver a post-deployment readout with attribution limits clearly stated.
 * Maintain client-facing engineering presence and a feedback loop into the platform team - running discovery, working sessions with client IT/data teams, demos, and surfacing the data shapes and failure modes only visible from inside client data.