DevOps Engineer

• 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

• 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

• Equity for all full-time employees
• Annual performance bonus
• Employer matched retirement plan
• Generously subsidized healthcare with 100% employer paid dental, vision, telemedicine, and virtual mental health counseling
• Parental leave
• Unlimited paid time off (PTO)