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, setting 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.

- Robust 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.

Disclaimer: This job posting has been aggregated from external source.

Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Principal Platform Engineer - Research Data Platform (Bengaluru)
🏢 Eli Lilly
📍 Bengaluru