Design, build, and operate MLOps pipelines supporting the full ML lifecycle (training, validation, deployment, monitoring)
Enable production workloads for AI/ML and Generative AI systems, including LLM‑based services
Develop and maintain CI/CD pipelines for AI/ML services and supporting infrastructure
Build and manage cloud‑native infrastructure on AWS, with heavy use of Kubernetes and containerized workloads
Automate infrastructure provisioning and configuration using Infrastructure as Code (Terraform)
Implement model versioning, experiment tracking, and artifact management across environments
Ensure reliability, scalability, observability, and cost efficiency of AI platforms
Partner with AI/ML engineers to operationalize models and standardize deployment patterns
Implement monitoring and alerting for system health, model performance, and drift
Enforce security, compliance, and governance requirements for AI workloads
Participate in incident response, root cause analysis, and continuous improvement initiatives
Document standards, best practices, and reference architectures for MLOps and AI infrastructure.

Requirements

15+ years of experience in DevOps, SRE, or Platform Engineering, with AWS as a primary cloud
Experience supporting machine learning systems in production, including deployment and monitoring concerns
Hands-on experience with machine learning platforms, particularly AWS SageMaker (required)
Strong hands-on experience with Kubernetes, containerized workloads, and cloud networking
Proven experience building and operating CI/CD pipelines (e.g., GitLab CI, ArgoCD)
Strong proficiency with Terraform and scripting/programming in Python or similar languages
Solid Linux, systems, and troubleshooting fundamentals
Excellent communication skills and ability to work across teams
Direct experience with MLOps platforms and tooling (model registries, experiment tracking, feature stores)
Exposure to Generative AI / LLM workloads in production environments
Familiarity with data stores commonly used in ML systems (e.g., Postgres, DynamoDB, object storage)
Experience operating in regulated or fintech environments
Background in cost optimization for compute‑intensive workloads
Strong written and verbal communication skills
AWS certifications are a plus.

Benefits

Employer matched retirement plan
Generously subsidized healthcare with 100% employer paid dental
Vision coverage
Telemedicine
Virtual mental health counseling
Parental leave
Unlimited paid time off (PTO)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

MLOpsCI/CD pipelinesAWSKubernetesTerraformPythonLinuxmachine learningGenerative AImodel versioning

Soft Skills

communicationcollaborationproblem-solvingincident responseroot cause analysiscontinuous improvementdocumentationteamworkorganizational skillsadaptability

Certifications

AWS certifications