Hybrid Cloud AI Engineer for Agentic Workflows

Hewlett Packard Enterprise; San Juan (PR); Hybrid; USD 140,000 - 220,000; Full time

Benefits offered by this job

Health & Wellbeing benefits

Personal & Professional Development

Inclusion & Flexibility

Job summary

Hewlett Packard Enterprise is seeking an Agentic AI Cloud Development Engineer to design and operationalize agentic workflows powering Marvis. The role blends data science, AI, and production engineering to build reliable, observable cloud-scale AI systems.

The candidate will work in a hybrid model, collaborate with network engineers, and contribute to tool-serving protocols, with a focus on LangGraph, MCP, and scalable architectures. 2–4 years in production ML/AI is typical."

Qualifications

• Bachelor’s or Master’s in computer science or related field.
• 2–4 years of production ML/AI system experience.
• Hands-on with generative AI and LLM-based apps.

Responsibilities

• Design and iterate agentic workflows using LangGraph.
• Develop MCP servers and tools, including tool schemas and playbooks.
• Build data streaming or batch processing over Kafka/Spark.
• Integrate and optimize LLM capabilities at production scale.
• Collaborate with network engineers and product managers to formalize problems as agentic workflows.
• Develop data transformation logic in sandboxed execution environments.
• Deploy and operate containerised services in Kubernetes with CI/CD pipelines.

Skills

Python

FastAPI

Spark

Kafka

Kubernetes

LLM

Education

Bachelor's degree in CS

Master's degree preferred

Tools

LangGraph

LangChain

Claude SDK

Docker

GitHub Actions

Job description

Hewlett Packard Enterprise is seeking an Agentic AI Cloud Development Engineer to design and operationalize agentic workflows powering Marvis. The role blends data science, AI, and production engineering to build reliable, observable cloud-scale AI systems.

The candidate will work in a hybrid model, collaborate with network engineers, and contribute to tool-serving protocols, with a focus on LangGraph, MCP, and scalable architectures. 2–4 years in production ML/AI is typical."