Senior Cloud DevOps AI/ML Engineer AWS Platform & MLOps

Role Summary

Own our secure, multi-account AWS foundation and the MLOps/GenAI platform that powers clinician matching, document processing, and safety tooling. In the role of Senior Cloud DevOps AI/ML Engineer AWS Platform & MLOps, you will blend SRE discipline with ML platform pragmatism to deliver compliant, observable, and cost-efficient infrastructure.

Key Responsibilities

• Build and operate a secure AWS landing zone including Organizations, Control Tower, VPC architecture, private networking, and multi-account guardrails.
• Design CI/CD and IaC at scale using GitHub Actions, CodeBuild, CodePipeline, Terraform and/or AWS CDK; implement policy-as-code with Open Policy Agent and AWS SCPs.
• Manage compute fabrics with Amazon EKS (preferred) and ECS Fargate; configure autoscaling, HPA/Karpenter, and enhance cluster security with IRSA and PodSecurity.
• Develop an observability platform using AWS Distro for OpenTelemetry, CloudWatch, Prometheus/Grafana, and X-Ray with focus on golden signals, SLOs, incident response and on-call processes.
• Enforce security-by-default through IAM least-privilege, KMS envelope encryption, Secrets Manager/Parameter Store, AWS WAF/Shield, artifact signing, and SBOM/SLSA.
• Drive resiliency engineering with multi-AZ baselines, chaos testing, backup/DR using AWS Backup plus cost management through CUR, budgets and rightsizing.
• Implement MLOps practices using SageMaker projects/pipelines, model registry, feature store, inference endpoints and safe deployment patterns.
• Integrate GenAI capabilities with Amazon Bedrock for guardrails, content filters, and PII redaction, leveraging vector indexes via pgvector or OpenSearch k-NN.
• Enable secure data platform solutions with S3, Lake Formation, Glue, Athena, and EMR ensuring governance and auditability.
• Champion DevSecOps practices including threat modeling, SBOM scanning, container/image hardening, and secure software supply chain measures.

Desired Candidate Profile

A deep background with 7+ years in building and operating cloud platforms. Must have hands-on experience with AWS networking, IAM, compute, storage, and security. Strong expertise in Terraform and/or AWS CDK, GitOps, CI/CD technologies, Linux, containers, and Kubernetes (EKS) in production. Operational excellence through SRE practices, managing SLO/error budgets, incident management and on-call responsibilities is essential. MLOps experience using SageMaker or equivalent, data pipelines for feature engineering, and experience with real-time/batch inference is required. Familiarity with Bedrock, OpenSearch, and pgvector for RAG and vector search is a plus along with a comprehensive understanding of security and compliance standards such as GDPR.

How We Work & Benefits

Work on end-to-end platform architecture with a small, senior team. Enjoy a remote-friendly work culture with pairing sessions, design reviews, and continuous improvement. Your contribution to reliable ML tooling will make a daily impact by improving access to care.

Compliance & Notes

All workloads run in EU regions (e.g., eu-central-1) with strict data residency and encryption requirements. GenAI usage follows privacy-preserving norms with opt-in consent, PII/PHI redaction, and comprehensive audit trails.

Key skills/competency

• AWS
• MLOps
• DevOps
• SRE
• Terraform
• CI/CD
• Security
• Observability
• Kubernetes
• GenAI