Lead Machine Learning Operations Specialist
2 weeks ago
We are seeking an experienced Senior MLOps Engineer to join our team. The ideal candidate will have a strong background in machine learning operations, with experience in productionizing models, building automation-first delivery pipelines, and engineering scalable serving architectures.
Main Responsibilities:- Develop and implement model productionization workflows, ensuring resilient services with clear Service Level Objectives (SLOs), runbooks, and fast, safe rollbacks.
- Design and implement automation-first delivery pipelines using GitLab CI and Terraform-based Infrastructure as Code (IaC).
- Engineer scalable serving architectures for batch and real-time inference on Amazon Elastic Container Service (EKS/ECS/Lambda) and Databricks Model Serving with probes, autoscaling, and canary/blue-green deployments.
- Instrument end-to-end observability (data, model, system) and lead incidents and post-mortems that drive durable fixes.
- Partner across teams to translate requirements into designs, Architecture Decision Records (ADRs), and change plans, balancing security, privacy, cost, and performance tradeoffs.
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field and 3+ years of relevant experience; or High School Diploma/General Education Degree and 6+ years of relevant experience.
- 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.
This position involves collaboration with various stakeholders to develop and maintain reliable machine learning systems. Strong technical skills and excellent problem-solving abilities are required for success in this role.
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