Senior MLOps Engineer

Essential Duties

Include, but are not limited to, the following :

Own productionizing models—from 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.