Agentic AI Cloud Development Engineer

Agentic AI Cloud Development EngineerThis role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.

Job Description:

Agentic AI Cloud Development Engineer builds and operationalizes the core agentic workflows that power Marvis, Juniper's next-generation AI assistant for network operations. Working at the intersection of data science, generative AI, and production engineering, this role is responsible for designing, implementing, and evaluating the reasoning pipelines, tool-calling patterns, skills, and MCP server integrations that enable Marvis to autonomously diagnose, troubleshoot, and resolve complex networking problems. The ideal candidate combines practical experience with LLM-based agentic frameworks (LangGraph preferred) with the software engineering discipline needed to deliver reliable, observable AI systems in a cloud-native environment

Management Level Definition:

Contributions impact technical components of products, solutions, or services regularly and sustainably. Applies advanced subject matter knowledge to solve complex business and technical problems and is regarded as a subject matter expert in agentic AI and applied GenAI. Provides expertise and partnership to functional and technical project teams and may participate in cross-functional initiatives. Exercises significant independent judgment to determine best method for achieving objectives. May lead projects and mentor colleagues.

Responsibilities:

• 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.

Education and Experience Required:

• 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

Knowledge and Skills:

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.

Health & Wellbeing

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.

Unconditional Inclusion

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. 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.

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Job:

Engineering

Job Level:

TCP_02

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