Jobs · Engineering

DevOps / AgentOps Engineer

Revelation Pharma · United States · 3 wk ago
RemoteRemoteEngineeringFull-time

Job Activities and Responsibilities

  • Design, build, and maintain CI/CD pipelines for application code, FHIR data layer, and AI agent deployments across dev/staging/production environments
  • Own infrastructure-as-code (Terraform or AWS CDK) for all AWS services including HealthLake, Bedrock, Clean Rooms, Lambda, Step Functions, S3, and IAM
  • Build and operate the observability stack — logging, metrics, tracing, and alerting — with particular attention to agent execution observability (token usage, latency, state transitions, error rates)
  • Implement and maintain HIPAA-compliant infrastructure patterns: encryption at rest and in transit, key rotation, audit log pipelines, BAA-covered service configurations
  • Work directly alongside KMS engineers during the build phase — reviewing their IaC, ensuring deployment standards are met, and absorbing system knowledge for long-term internal ownership
  • Manage shared services infrastructure on the Revelation Pharma side, including cross-environment networking, secrets management, and service mesh configuration
  • Automate agent deployment, versioning, rollback, and A/B testing workflows for Bedrock-based agents
  • Establish and enforce environment parity, secrets hygiene, and least-privilege IAM policies across all workstreams

Required Technical Skills (Staff-Level Depth Expected)

  • AWS Infrastructure (Expert): Deep hands-on experience with IAM policy design, VPC networking, KMS encryption, CloudWatch/CloudTrail, and at least 3 of: HealthLake, Bedrock, Lambda, Step Functions, ECS/Fargate, S3, Clean Rooms.
  • Infrastructure-as-Code (Expert): Production experience with Terraform or AWS CDK. Must have built and maintained IaC for regulated environments — drift detection, state management, module composition, and policy-as-code (OPA/Sentinel or equivalent).
  • CI/CD & Deployment (Expert): End-to-end pipeline design (GitHub Actions, CodePipeline, or equivalent). Blue/green and canary deployment patterns. Container orchestration (ECS Fargate preferred). Artifact management and promotion workflows across environments.
  • Observability & Monitoring (Strong): Production experience building observability stacks — structured logging (CloudWatch Logs Insights or ELK), distributed tracing (X-Ray or OpenTelemetry), custom metrics and dashboards, PagerDuty/Opsgenie integration. Must understand healthcare-specific audit logging requirements.
  • Security & Compliance (Strong): HIPAA technical safeguard implementation experience. Encryption key management (KMS), secrets management (Secrets Manager/SSM Parameter Store), network segmentation, and WAF/Shield configuration. Familiarity with HITRUST or SOC 2 control frameworks is a plus.
  • Agent/AI Operations (Developing to Strong): Experience deploying and monitoring LLM-based systems in production. Understanding of prompt versioning, model endpoint management, token budget monitoring, and inference cost optimization. Experience with Bedrock specifically is preferred but not required — equivalent experience with other LLM deployment platforms (Azure OpenAI, Vertex AI, SageMaker) is acceptable.

Required Experience

  • 5+ years in DevOps, SRE, or platform engineering roles with at least 2 years at a staff or senior level
  • Production infrastructure experience in healthcare, fintech, or another regulated industry with formal compliance requirements
  • Experience working in blended team models (internal + outsourced) — must be comfortable establishing standards that external teams follow
  • Demonstrated ability to work autonomously in early-stage environments where you are defining process, not inheriting it

Preferred Qualifications

  • AWS certifications (Solutions Architect Professional, DevOps Engineer Professional, or Security Specialty)
  • Experience with FHIR server infrastructure (HealthLake, HAPI FHIR, or Smile CDR)
  • Familiarity with Bedrock AgentCore, agent orchestration patterns, or multi-agent system deployment
  • Experience with cost optimization for AI/ML workloads (inference endpoint sizing, spot instances, reserved capacity)

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