DevOps Engineer – Generative AI & Enterprise Web Platforms

DevOps Engineer – Generative AI & Enterprise Web Platforms
This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.
Who We Are:
Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.
Job Description:
Job Family Definition:
Designs, develops, troubleshoots and debugs software programs for software enhancements and new products. Develops software including operating systems, compilers, routers, networks, utilities, databases and Internet-related tools. Determines hardware compatibility and/or influences hardware design.
Management Level Definition:
Contributions have visible technical impact on a product or major subcomponent. Applies in-depth professional knowledge and innovative ideas to solve complex problems. Visible contributions improve time-to-market, achieve cost reductions, or satisfy current and future unmet customer needs. Recognized internal authority on key technology area applying innovative principles and ideas. Provides technical leadership for significant project/program work. Leads or participates in cross-functional initiatives and contributes to mentorship and knowledge sharing across the organization.
Role Overview
We are seeking a hands-on DevOps Engineer with experience to build, automate, and operate secure, reliable delivery platforms for enterprise applications powered by Generative AI, large language models, APIs, distributed systems, and modern web technologies.
The role combines CI/CD engineering, container orchestration, cloud operations, infrastructure automation, observability, security, performance, and production support. The successful candidate will collaborate with development, quality, networking, and platform teams to improve release reliability, troubleshoot cross-layer issues, automate repeatable operations, and strengthen service resilience across on-premises and AWS environments.
Responsibilities:
CI/CD and release engineering: Design, maintain, and improve automated build, test, security-scan, deployment, rollback, and release pipelines across development and production environments.
Container platforms: Operate Docker-based workloads and Kubernetes or k3s clusters, including on-premises developer sandboxes, configuration, upgrades, capacity, and troubleshooting.
Cloud and infrastructure automation: Provision and manage secure AWS infrastructure using repeatable automation, sound identity and access controls, networking, storage, compute, and environment configuration practices.
Observability and reliability: Implement metrics, logs, traces, dashboards, alerts, service-level indicators, and operational runbooks; support incident response, root-cause analysis, capacity planning, and resilience improvements.
Automation and validation: Build reusable Python and pytest utilities and integrate API, performance, reliability, security, and end-to-end validation into CI/CD workflows.
GenAI platform operations: Support deployment, tracing, regression checks, safety controls, latency monitoring, failure recovery, and cost visibility for LLM and agent-based services using tools such as LangSmith, LangGraph, Langfuse, and MCP.
Collaboration and continuous improvement: Partner with engineering, quality, security, and networking teams, participate in design and operational reviews, mentor less-experienced engineers, and drive practical improvements to platform standards and developer experience.
Education and Experience Required:
Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline.
4-6 years of relevant experience in DevOps, Site Reliability Engineering, platform engineering, cloud operations, or production engineering
Knowledge and Skills:
CI/CD and automation: Strong hands-on experience with pipeline engineering, Git-based workflows, Python, pytest, scripting, artifact management, automated testing, diagnostics, and deployment automation.
Containers and orchestration: Production experience with Docker, Kubernetes, and k3s, including cluster operations, workload deployment, configuration, scaling, upgrades, and troubleshooting.
AWS and infrastructure: Hands-on knowledge of AWS services, identity and access management, compute, storage, networking, monitoring, infrastructure automation, security, and cost-aware operations.
Observability and operations: Experience with production monitoring, logging, tracing, alerting, dashboards, incident response, root-cause analysis, high availability, disaster recovery, and tools such as Datadog.
APIs and performance: Experience validating REST or gRPC services and using tools such as JMeter, SoapUI, and Postman for functional, integration, performance, scale, reliability, and security testing.
Networking and protocols: working knowledge of networking protocols like BGP, MPLS, EVPN, VXLAN, NETCONF, RESTCONF, gRPC, SNMP, LLDP and traffic tools such as Ixia or Spirent.
GenAI operations: Familiarity with LLM and agent evaluation, groundedness, hallucination and safety checks, regression metrics, tracing, human review, LangSmith, LangGraph, Langfuse, and MCP.
Problem-solving and collaboration: Ability to analyse architecture, diagnose cross-layer failures, assess operational risk, communicate root caus