IT Engineer V

MLOps Platform Engineer

The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations capabilities. This role will play a key part in enabling scalable, reliable, and secure ML model development and deployment across our cloud and container platforms.

This is a hands‑on engineering role requiring strong expertise in AWS, Kubernetes (EKS), CI/CD automation, containerization, and ML platform operations. The ideal candidate will have solid engineering fundamentals combined with practical knowledge of ML workflows, deployment patterns, and platform reliability.

• Engineer, manage, and support MLOps platform components across AWS and EKS-based environments.
• Oversee deployment, configuration, and operation of infrastructure used for ML training, batch inference, and real‑time model serving.
• Ensure platform availability, resilience, and performance across dev, test, and production environments.
• Implement role‑based access controls (RBAC), network policies, and scalable namespace designs within EKS.

• Build and support CI/CD pipelines (Git Lab) for model packaging, container image builds, vulnerability scanning, and automated deployment flows.
• Enable standardized model release processes including environment promotion, versioning, and rollback workflows.
• Integrate CI/CD with ML frameworks, model repositories, artifacts, and runtime environments.

• Design and manage EKS workloads supporting containerized ML jobs and microservices.
• Implement auto‑scaling, resource quotas, cluster optimization, and multi‑tenant workload isolation.
• Support GPU and CPU‑based training/inference workloads.

• Implement logging, monitoring, and alerting for ML pipelines, model endpoints, batch jobs, and platform components.
• Analyze compute, storage, and data transfer usage to optimize cost efficiency across ML workloads.
• Perform incident response, root cause analysis, and long‑term remediation planning.

• Partner with Data Scientists, ML Engineers, and application teams to operationalize end‑to‑end machine learning solutions.
• Provide technical guidance on best practices for ML model lifecycle management, deployment patterns, and scalable architectures.
• Contribute to documentation, runbooks, onboarding materials, and internal knowledge bases.

• 3+ years of hands‑on experience with AWS services, including EKS, EC2, S3, IAM, Cloud Watch, and ECR.
• Strong experience operating and troubleshooting Kubernetes (preferably AWS EKS).
• Proficiency in containerization (Docker) and orchestration concepts.
• Strong programming/scripting experience in Python and Bash.
• Experience building and managing CI/CD pipelines (Git Lab or equivalent).
• Familiarity with machine learning workflows, including training, inference, and model monitoring.
• Experience with infrastructure‑as‑code (Terraform or Cloud Formation).
• Experience supporting production platforms, including incident management and root cause analysis.

• Experience managing Data Analytics Platforms / Tools (e.g., Domino, Sage Maker)
• Experience with ML lifecycle tools such as MLflow, or similar.
• Experience supporting GPU‑based workloads or distributed training environments.
• Familiarity with enterprise MLOps architectures and patterns (batch, real‑time, microservices).
• Understanding of data processing frameworks and feature pipelines.

• Strong analytical, troubleshooting, and problem‑solving skills.
• Effective communication and documentation abilities.
• Ability to collaborate across engineering, analytics, and product teams.
• Self‑motivated with the ability to drive initiatives independently.
• Ability to work in a complex, regulated enterprise environment.

EEO:
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.