Senior ML Platform Engineer - Production & MLOps

Attain; Chicago, Northern (IL, KY); Hybrid; USD 170,000 - 240,000; Full time

Job summary

Attain is seeking a Senior Machine Learning Engineer to own our production ML systems and build the MLOps platform infrastructure powering our B2C financial services. This role is hands-on and infrastructure-first, focusing on pipelines, feature infrastructure, model-serving, CI/CD, and observability in production.

You will design, build, and operate the end-to-end ML pipelines and tooling, enabling data scientists to deploy and retrain models quickly and safely while keeping systems healthy and

Qualifications

• 5+ years of direct experience as a Machine Learning Engineer, ML Platform Engineer, MLOps Engineer, Applied Scientist or similar role building and operating production ML systems
• Degree in STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or related quantitative field
• Strong expertise deploying, serving, monitoring, and operating ML models in production—including feature engineering systems, training/serving parity, retraining, and model performance diagnostics
• Experience building low-latency online model serving for real-time decisioning
• Hands-on MLOps experience: pipelines, CI/CD for ML, containerization (Docker), orchestration (Kubernetes), infrastructure-as-code (Terraform), and workflow schedulers (Airflow)
• Experience with model versioning, reproducibility, and safe progressive rollout of models in production
• Fluently directing AI coding agents to build, operate, and debug production ML systems
• Track record of replacing manual ML workflows with durable automation
• Experience building infrastructure behind high-impact ML use cases such as credit decisioning, risk modeling, fraud, churn
• Familiarity with model explainability and auditing for regulated decisioning
• Strong software and platform engineering fundamentals
• Strong Python coding skills; Go or Rust is a plus
• Experience with distributed computing and GPU-accelerated workloads (Spark, Ray, Dask)
• Strong SQL skills and cloud data warehouses (BigQuery, Spanner)
• Experience with observability tools (Prometheus, Grafana, Datadog)
• Experience with cloud platforms; GCP preferred
• Willingness to wear multiple hats across engineering, infra, and ML execution
• Strong written and verbal communication skills

Responsibilities

• Build, deploy, and operate production ML systems focusing on reliability, performance, and fast execution
• Improve pipelines and serving infrastructure behind predictive models across decisioning, fraud, churn, and transaction intelligence
• Own production model lifecycle: features, deployment, CI/CD, monitoring, retraining
• Develop reusable modeling pipelines, feature engineering systems, model-serving infra, and production-quality code deployed via Terraform and CI/CD in GCP + Kubernetes
• Instrument models with monitoring, alerting, and retraining; define metrics/dashboards to surface drift
• Direct AI coding agents to write, test, and ship infra and pipeline code with prudent verification
• Automate repetitive ML lifecycle steps to speed up development
• Collaborate with data scientists to deploy, iterate, and retrain models in production
• Partner with analysts, platform engineers, product managers, and stakeholders to deliver ML systems with quality and efficiency
• Identify areas for platform improvements, automation, and MLOps tooling to boost velocity

Skills

Python

MLOps

CI/CD for ML

SQL

Distributed computing

Cloud data warehouses

Prometheus/Grafana

Go or Rust

Education

B.S. in STEM field

Tools

Docker

Kubernetes

Terraform

Airflow

GCP

BigQuery

Istio

Prometheus/Grafana

Job description

Attain is seeking a Senior Machine Learning Engineer to own our production ML systems and build the MLOps platform infrastructure powering our B2C financial services. This role is hands-on and infrastructure-first, focusing on pipelines, feature infrastructure, model-serving, CI/CD, and observability in production.

You will design, build, and operate the end-to-end ML pipelines and tooling, enabling data scientists to deploy and retrain models quickly and safely while keeping systems healthy and