Senior MLOps Engineer

Role & responsibilities

Essential Duties

Include, but are not limited to, the following:

• Own productionizing modelsfrom tracked experiments to governed releases—ensuring resilient services with clear SLOs, runbooks, and fast, safe rollbacks.
• Build automation-first delivery: reproducible builds, layered tests, and environment promotion via GitLab CI and Terraform-based IaC.
• Engineer scalable serving: batch and real-time inference on EKS/ECS/Lambda and Databricks Model Serving with probes, autoscaling, and canary/blue-green deployments.
• Instrument end-to-end observability (data, model, system); detect drift/regressions; lead incidents and post-mortems that drive durable fixes.
• Partner across teams to translate requirements into designs, ADRs, and change plans; balance security, privacy, cost, and performance tradeoffs.
• Continuously reduce toil through automation, optimize model/GPU/LLM cost, and evolve templates/playbooks for repeatable delivery.

Minimum Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field and 3+ years of relevant experience as outlined in the essential duties; or High School Diploma/General Education Degree and 6+ years of relevant experience as outlined in the essential duties in lieu of Bachelor’s Degree.
• 3+ years operating ML systems in production (MLOps).
• Experience with Python for ML engineering (packaging, typing, testing, performance)
• Experience developing GitLab CI for ML/GenAI (multi-stage pipelines, artifacts, evaluation/security gates) and Terraform for ML/GenAI (reusable modules, drift detection); secure packaging & containerization.
• Experience deploying and operating compute for ML (EKS/ECS/Lambda), and secure data access patterns (S3/VPC/IAM/KMS, private endpoints)
• Experience implementing MLflow tracking, model registry & governed promotion, packaging & deployment to multi-target runtimes.
• Experience operating real-time + batch/streaming inference workloads, ML observability, layered testing (unit/integration), workflow orchestration, and cost optimization.
• Experience designing and implementing IAM least-privilege, secrets/key management for CI/CD pipelines; privacy and compliance awareness.

Preferred Qualifications:

• Advanced GitLab CI (dynamic child pipelines, components, cross-project triggers, security scans, compliance gates).
• Advanced Terraform (policy-as-code, gated plan/apply, environment promotion).
• Advanced real-time serving (multi-tenant routing, dynamic model loading) and SLO-driven rollback/automation.
• Databricks governance (Unity Catalog, lineage) and feature platform approval/reuse workflows.