Senior ML Engineer — Client-Facing Production AI for Impact

AZX; United States; Remote; USD 140,000 - 230,000; Full time

Benefits offered by this job

Health insurance

Equity

Fully remote culture

Bonus eligibility

Flexible PTO

Job summary

AZX seeks an ML Engineer to build ML systems directly inside client environments, starting with client data spread across multiple systems and ending with a model running on a schedule inside their environment. You will wear many hats in a small team — some DevOps, some infrastructure, some frontend and backend — because you are the engineering face of AZX to your client.

Responsibilities include owning the full ML delivery lifecycle, building forecasting and detection models, backtesting and

Qualifications

• 5+ years of shipping applied ML to production with forecasting/detection/optimization.
• Strong data engineering to find/clean/join data at scale without a dedicated team.
• Rigorous validation: chrono splits, walk-forward, as-of correctness.
• Ship real systems: Python, SQL, tests, Docker, scheduler, API/app surface, monitoring.
• Client-facing capability: run discovery, demos, and push back with alternatives.

Responsibilities

• Own the full ML delivery lifecycle from data discovery to deployment and retraining.
• Build forecasting/detection models robust to real-world data issues.
• Backtest and justify precision/recall tradeoffs to stakeholders.
• Design systems distinguishing no prediction vs wrong prediction.
• Ship usable capability: FastAPI service and a React surface, scheduled jobs.

Skills

ML delivery

Data engineering

Validation discipline

Software engineering

Client-facing

ML judgment

Core stack

Cloud tooling

LLMs for agentic work

Education

Bachelor's degree

Master's degree

Tools

Python

SQL

Docker

FastAPI

React/TypeScript

Postgres/TimescaleDB

Azure/AWS

Job description

AZX seeks an ML Engineer to build ML systems directly inside client environments, starting with client data spread across multiple systems and ending with a model running on a schedule inside their environment. You will wear many hats in a small team — some DevOps, some infrastructure, some frontend and backend — because you are the engineering face of AZX to your client.

Responsibilities include owning the full ML delivery lifecycle, building forecasting and detection models, backtesting and