Staff Platform Engineer, AI/ML Infrastructure

Department: AI Software & Operations

The Staff Platform Engineer, AI/ML Infrastructure will provide technical leadership for thecloud platforms, deployment systems, and operational foundations that power enterprise-scalegenerative AI applications.

This role will define and evolve the infrastructure architecture for AI/ML platforms running across AWS,Kubernetes, serverless, and containerized environments. The engineer will lead platform standards forreliability, scalability, observability, CI/CD, security, and developer enablement, while partnering closelywith software engineering, AI engineering, security, and operations teams.

The ideal candidate combines deep hands-on cloud engineering experience with staff-level technicalinfluence. They are comfortable designing infrastructure patterns, writing infrastructure-as-code,improving delivery pipelines, mentoring engineers, and making architectural decisions that raise theoperational maturity of AI platforms across multiple teams.

• Define and drive the technical strategy for AI/ML platform infrastructure supporting generative AIapplications, LLM integrations, model routing, and enterprise AI services.
• Architect, build, and operate scalable cloud platforms using AWS services such as EKS, ECSFargate, Lambda, DynamoDB, S3, OpenSearch, Secrets Manager, CloudWatch, ALB, and MWAA.
• Establish reusable infrastructure patterns using CloudFormation, Helm, and Terraform to supportreliable multi-environment and multi-region deployments.
• Lead CI/CD architecture using GitHub Actions, reusable workflows, OIDC-based AWSauthentication, automated quality gates, deployment promotion, and environment approvals.
• Design and improve observability across AI platforms, including CloudWatch dashboards, logs,alarms, Prometheus/Grafana, OpenSearch, Langfuse, and LLM-specific operational metrics.
• Build platform capabilities for GenAI workloads, including model availability monitoring.
• Partner with software engineering teams to improve deployment reliability, rollback strategies,health checks, autoscaling, load testing, and runtime performance.
• Define and enforce security and compliance practices for infrastructure, including IAM permissionboundaries, Secrets Manager usage, secret scanning, audit logging, tagging standards, andchange-management controls.
• Provide technical leadership for cost optimization, capacity planning, environment standardization,and operational resilience across development, test, production, and sandbox environments.
• Mentor engineers, review architecture and infrastructure designs, and influence platformengineering practices across teams.

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a relatedtechnical field, or equivalent practical experience.
• 7+ years of experience in DevOps, platform engineering, cloud infrastructure, site reliabilityengineering, or software engineering roles.
• Strong hands-on experience with AWS/Azure/GCP infrastructure and services, including container,serverless, networking, storage, observability, and security services.
• Experience designing and operating production systems on Kubernetes, ECS/Fargate, orcomparable container orchestration platforms.
• Proficiency with infrastructure-as-code, especially CloudFormation, Terraform, Helm, or similartooling.
• Strong CI/CD experience with GitHub Actions or similar platforms, including reusable workflows,automated testing, deployment gates, and cloud authentication.
• Experience building and operating observability solutions using CloudWatch, Prometheus/Grafana,OpenSearch, or similar tools.
• Strong understanding of cloud security practices, IAM, secrets management, least-privilegeaccess, audit logging, and compliance requirements.
• Experience supporting distributed systems, microservices, APIs, asynchronous workloads, andmulti-environment deployments.
• Demonstrated ability to lead technical design, mentor engineers, and influence engineeringpractices across teams.

• Experience supporting AI/ML or generative AI platforms, including LLM gateways, model routing,prompt observability, token metering, or model failover.
• Experience operating platforms in regulated enterprise environments, ideally healthcare,pharmaceutical, finance, or life sciences.
• Experience with multi-account, multi-region AWS architectures and enterprise governancepatterns.
• Experience with cost optimization, autoscaling strategies, capacity planning, and cloud budgetmonitoring.
• Experience with load testing and performance validation using tools such as Locust or comparableframeworks.
• Strong Python or scripting skills for