Platform Engineer, Cloudops Infrastructure

Iambic Iambic · Pharma · Dublin, Ireland · Technology

Iambic is seeking a Platform Engineer, CloudOps Infrastructure to build and own the platform for their ML-driven drug-discovery workflows. This role involves creating deployment templates, an orchestration layer for model training and inference, and associated tooling, CI/CD, and observability. The position requires hands-on DevOps/CloudOps experience to operate and improve the system, adapting the platform to new ML research requirements. The ideal candidate will treat infrastructure as a product and ensure reliable and reproducible execution of ML workloads.

What you'd actually do

  1. Build and maintain standardized, reproducible, secured deployment templates.
  2. Develop and operate the orchestration layer (orchestration workflows, e.g. Prefect) and the GPU-backed compute paths that run training and inference, on a schedule and on demand.
  3. Own infrastructure-as-code, CI/CD pipelines, and the tooling that makes standing up, updating, and tearing down an environment routine and auditable.
  4. Build observability - metrics, logging, alerting - that gives a clear picture of system health across environments.
  5. Run and improve the system day to day (DevOps/CloudOps): drive operational practices that emphasize stability, predictability, and low overhead, and partner with the infosec function on infrastructure security posture.

Skills

Required

  • Strong Terraform/OpenTofu engineering skills
  • hands-on AWS (or comparable cloud) experience
  • Production experience with containerization
  • container orchestration (e.g. ECS)
  • CI/CD pipelines
  • Infrastructure-as-code
  • reproducible environments
  • Solid understanding of distributed systems fundamentals
  • Strong operational instincts: observability, debuggability, and maintainability

Nice to have

  • Experience operating multi-tenant or large-fleet platforms
  • Experience with workflow orchestration (Prefect, Airflow, Dagster, or similar)
  • experience with GPU compute platforms
  • Familiarity with GPU-backed environments
  • Familiarity with ML training/inference pipelines
  • Awareness of infrastructure security posture and compliance frameworks (e.g. ISO/IEC 27001 or similar)

What the JD emphasized

  • ML-driven drug-discovery workflows
  • run our drug-discovery ML workloads reliably and reproducibly
  • orchestration layer that runs model training and inference
  • ML researchers introduce new workflows and models

Other signals

  • ML-driven drug-discovery workflows
  • run our drug-discovery ML workloads reliably and reproducibly
  • orchestration layer that runs model training and inference
  • ML researchers introduce new workflows and models