Agentic AI Cloud Development Engineer

• Design, implement, and iterate on agentic workflows using LangGraph.
• Develop and maintain MCP (Model Context Protocol) servers and skills - defining tool schemas, implementing domain-specific tools, writing skill playbooks (SKILL.md), and managing server lifecycle (versioning, deployment, monitoring).
• Application design and development in streaming or batch mode over Kafka and Spark
• Integrate and optimize LLM capabilities at production scale, including structured outputs, streaming, function/tool calling, prompt engineering, and robust error handling across agent execution paths.
• Collaborate with domain experts (network engineers, product managers) to formalize networking problems as agentic workflows, translating troubleshooting playbooks into skills, tools, and data pipelines.
• Develop data analysis and transformation logic that runs in sandboxed execution environments (Code Mode), including multi-tool orchestration scripts, data aggregation, and visualization.
• Deploy and operate containerized services in Kubernetes, contributing to CI/CD pipelines, container image management, health probes, and resource optimization.
• Own observability for agentic workflows - implementing tracing, logging, cost tracking, and performance monitoring to ensure reliability of non-deterministic systems in production.

• Bachelor's, Master's in computer science degree.
• Typically, 2-4 years of experience building production ML/AI systems, with at least a year of hands-on work with generative AI and LLM-based applications

Data Engineering (Required):

• Proficient Python developer with experience in production codebases (not just notebooks).
• Backend service development: FastAPI or equivalent; REST/GraphQL API design.
• Expertise in big data technologies such as Apache Spark, Kafka with at least 2 year of relevant experience

Agentic AI & GenAI (Required):

• Experience with agentic orchestration frameworks: LangGraph (strongly preferred), LangChain, Claude Agent SDK, or equivalent - beyond prototypes.
• Hands-on experience with MCP (Model Context Protocol) or equivalent tool-serving protocols: tool schema design, server implementation, registry management.

Infrastructure & Production Systems (Required):

• Kubernetes: deploying, scaling, and managing workloads (Deployments, Services, ConfigMaps, Secrets, health probes).
• CI/CD pipelines for automated build, test, and deploy (Jenkins, GitHub Actions, ArgoCD, or similar).
• Container image management: building, tagging, versioning via Docker; familiarity with a container registry (ECR, GCR).
• Observability for AI systems: experience with tracing, monitoring, and logging tools (LangFuse, Prometheus, or equivalent)..

Additional Preferred Skills:

• Networking domain knowledge (wireless/wired diagnostics, network troubleshooting) is preferred but not required.
• Experience with AWS Bedrock, OpenSearch Serverless, or similar managed AI/ML services.
• Great written and verbal communication skills; ability to articulate technical designs to senior leadership

Accessibility

HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here .

Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.