Senior ML Engineer — Production AI for Clients (Remote)

Openkrill; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Equity

Flexible PTO

Fully remote culture

Job summary

AZX is seeking an ML Engineer to deploy ML systems directly inside client environments. You will start with data across multiple systems and end with a model running on a schedule within the client environment.

You will wear many hats in a small team, spanning DevOps, infrastructure, and frontend/backend work, serving as the engineering face of AZX to clients. The role requires strong ML fundamentals and hands-on engineering excellence.

Qualifications

• 5+ years of shipping applied machine learning to production — forecasting, detection/classification on time series, survival/reliability modeling, or optimization.
• Strong data engineering skills to find, clean, join, and profile data at scale.
• Rigorous validation including chron. splits, walk-forward validation, and as-of correctness.

Responsibilities

• Own the full ML delivery lifecycle: data discovery, modeling, deployment in client environment, scheduling, monitoring, retraining policy.
• Build forecasting and detection models robust to real-world data quality issues.
• Backtest and defend precision/recall tradeoffs for operational decisions.
• Design systems to distinguish no prediction from wrong prediction.
• Ship usable product: FastAPI service, small React surface, scheduled job.
• Own the measurement story: baselines, KPIs, monitoring, post-deployment readouts.
• Maintain client-facing engineering presence and gather client data feedback.

Skills

5+ years of ML in production

Python

SQL

Docker

Time-series modeling

FastAPI

React/TypeScript

Education

Bachelor's Degree

Master's degree

Tools

PostgreSQL

TimescaleDB

Pandas

scikit-learn

Azure/AWS

Docker

FastAPI

React

Job description

AZX is seeking an ML Engineer to deploy ML systems directly inside client environments. You will start with data across multiple systems and end with a model running on a schedule within the client environment.

You will wear many hats in a small team, spanning DevOps, infrastructure, and frontend/backend work, serving as the engineering face of AZX to clients. The role requires strong ML fundamentals and hands-on engineering excellence.