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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # 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, 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:** September 4, 2026 - **Apply:** https://startup.jobs/senior-ml-engineer-client-solutions-azx-9819406 ## About the Role * 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. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)