Machine Learning Engineer
Build and operate infrastructure-first production ML systems, including pipelines, model serving, CI/CD, monitoring, and automated retraining. The role requires 5+ years of production ML experience, strong Python and platform engineering skills, and hands-on expertise with cloud, Kubernetes, and MLOps tooling.
About the job
Responsibilities
- Build, deploy, and operate production ML systems supporting financial services and real-time decisioning.
- Develop pipelines and serving infrastructure for predictive models used in consumer decisioning, fraud, churn, transaction intelligence, and related use cases.
- Own production model lifecycle infrastructure, including feature pipelines, deployment, CI/CD, monitoring, and automated retraining.
- Build reusable modeling pipelines, feature engineering systems, model-serving infrastructure, and production-quality tooling in GCP and Kubernetes environments using Terraform and CI/CD.
- Define monitoring, alerting, dashboards, and metrics to detect model drift, degradation, and system issues.
- Automate repetitive ML lifecycle workflows and enable data scientists to deploy, iterate on, and retrain models safely.
- Build low-latency online model-serving services for real-time decisioning.
- Implement model versioning, reproducibility, and progressive rollout strategies such as shadow, canary, and champion-challenger deployments.
- Use AI coding agents to write, test, ship, operate, and debug infrastructure and pipeline code, verifying their output with sound engineering judgment.
- Collaborate with data scientists, analysts, platform engineers, product managers, and business stakeholders.
Requirements
- 5+ years of experience building and operating production ML systems as a Machine Learning Engineer, ML Platform Engineer, MLOps Engineer, Applied Scientist, or similar.
- Strong experience deploying, serving, monitoring, and operating ML models in production, including feature engineering, training/serving parity, retraining, and model diagnostics.
- Hands-on MLOps experience with pipelines, ML CI/CD, Docker, Kubernetes, Terraform, and workflow schedulers such as Airflow.
- Strong software and platform engineering fundamentals.
- Strong Python skills for building pipelines, services, and production tooling; Go or Rust experience is a plus.
- Experience with distributed computing and GPU-accelerated workloads, including Spark, Ray, Dask, or distributed training and inference.
- Strong SQL skills and experience with cloud data warehouses and operational databases such as BigQuery and Spanner.
- Experience with observability tools such as Prometheus, Grafana, or Datadog.
- Experience with cloud computing platforms; GCP is preferred.
- Strong written and verbal communication skills.
- Ability to apply critical thinking, abstract reasoning, and engineering judgment to complex technical and business problems.
- Experience replacing manual ML workflows with durable automation.
- Willingness to work across engineering, infrastructure, and ML execution as business needs require.
Nice-to-haves
- STEM degree in Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field.
- Experience with service meshes such as Istio and low-latency gRPC or microservice serving.
- Experience directing AI coding agents such as Claude Code or Cursor.
- Experience supporting credit decisioning, risk modeling, fraud, churn, or consumer behavior modeling.
- Familiarity with model explainability, auditability, and compliance for regulated decisioning and fintech.
- Experience with Go or Rust.
Skills
Python, MLOps, Kubernetes, Docker, Terraform, Airflow, GCP, SQL, BigQuery, Spanner, Prometheus, Grafana, Spark, Ray
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