Principal Platform Engineer - Research Data Platform

Responsibilities

DevOps and Infrastructure

• Own and evolve the platforms delivery and runtime foundations across CI/CD, infrastructure-as-code, Kubernetes/EKS, and AWS services - ensuring platform components can be built, deployed, scaled, and rolled back safely.
• Design and maintain reliable deployment workflows using Jenkins, GitHub Actions, container registries, environment promotion strategies, and automated validation gates for application and data-platform services.
• Strengthen operational excellence through observability, logging, metrics, alerting, runbooks, and incident-response practices that improve service reliability, recoverability, and mean time to resolution.

AI, Agents and MCP Tooling

• Design and build AI agents and intelligent automation tools that help research scientists and data engineers interact with the platform more effectively.
• Design and implement MCP (Model Context Protocol) integrations that connect AI agents to platform data sources, tools, and services - enabling seamless, tool-augmented workflows for end users.
• Evaluate and integrate emerging AI tools, frameworks, and patterns into the platforms development workflows - finding opportunities where AI-assisted development, code generation, or intelligent automation can meaningfully accelerate the teams delivery.

Data Engineering and Platform Capabilities

• Bring enough familiarity with modern data-platform patterns to collaborate effectively with data engineering teams - including Airflow, Glue/Spark, Kafka/MSK, and Lakehouse oriented architectures.
• Apply platform engineering skills such as API design, service integration, automation, operational troubleshooting, and reusable tooling to strengthen the reliability and usability of data-platform capabilities.

Crosscutting

• Contribute to platform documentation, technical design proposals - ensuring that AI integrations, MCP patterns, and cross-cutting platform capabilities are well-documented and accessible.
• Rotate into feature or platform squads as needed - this role is deliberately not siloed to a single domain.
• Platform engineering mindset: able to design reusable tooling, APIs, service integrations, self-service workflows, and automation that improve developer and user productivity.

Required Qualifications

• Bachelors or masters degree in computer science, Information Technology, or a related technical field - required.
• 6-10 years of professional experience in DevOps, platform engineering, infrastructure engineering, with demonstrated ownership of production systems.
• Strong hands-on Python proficiency for automation, APIs, AI tooling, scripting, operational workflows, and platform integrations.
• Hands-on experience building, operating, and troubleshooting CI/CD pipelines using tools such as Jenkins, GitHub Actions , automated validation gates, and deployment promotion workflows.
• Solid working knowledge of AWS, infrastructure-as-code, containerized workloads, and Kubernetes/EKS-based runtime environments.
• Strong operational mindset across observability tools (such as Loki, Tempo or Grafana), logging, metrics, alerting, runbooks, incident response, root-cause analysis, and reliability improvement.
• Practical curiosity or hands-on experience with LLMs, AI agents, MCP, tool integrations, or AI-assisted automation ; production AI experience is helpful but not required.
• Experience writing unit tests, integration tests, and automated test suites, with a bias toward building quality and validation into delivery workflows.

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