Senior Machine Learning Ops Engineer

Role overview

Senior MLOps Engineer within the Data Platform organization on a newly formed MLOps team. You will help evolve and scale the enterprise machine learning platform by owning infrastructure, orchestration, deployment, observability, and reliability for business-critical data and ML pipelines.

Responsibilities

• Design, deploy, and maintain scalable ML infrastructure for model training, batch inference, and real-time inference workloads.
• Provision and deploy infrastructure and services across AWS, Snowflake, and related platforms using Infrastructure-as-Code (IaC).
• Build and maintain containerized model serving solutions using Docker and FastAPI, following modern deployment patterns.
• Document architecture, deployment standards, and operational processes for maintainability and reproducibility.
• Partner with Data Science and Data Engineering to productionize ML models and improve deployment velocity.
• Implement CI/CD, IaC, testing, and deployment automation best practices across ML systems.
• Establish observability and monitoring for deployed ML systems, including:
  • model performance monitoring
  • drift detection
  • data quality validation
  • automated alerting
• Optimize reliability, scalability, governance, and operational efficiency across ML workflows and supporting infrastructure.

Requirements

• 6 years experience in ML Ops, platform engineering, DevOps, or data platform engineering.
• 3+ years hands-on experience with AWS.
• Experience with IaC using Terraform, OpenTofu, or CloudFormation.
• Strong SQL skills and experience with a modern data warehouse such as Databricks, Snowflake, or BigQuery.
• CI/CD and modern software engineering best practices.
• Orchestration frameworks such as Dagster, Airflow, or Prefect.
• Testing with pytest, including unit, integration, and end-to-end testing.
• Bash and Unix-based environments.
• Strong Python skills, including API development and automation tooling.
• Production experience deploying and operating ML systems.
• Docker and containerized deployment experience.
• Backend services experience using FastAPI, Flask, or Django.
• Experience deploying ML systems on Kubernetes and/or ECS/EKS.
• Strong communication and cross-functional collaboration.

Preferred qualifications

• ML observability/experiment tracking tools (e.g., MLflow, Arize, Evidently, WhyLabs, Monte Carlo).
• Feature store / reusable ML data products.
• Experience supporting batch and low-latency inference.
• Experience in financial services/fintech or other regulated industries.