Finance AI & Automation Engineer – Enterprise GenAI

hpe; Spring (TX); On-site; USD 127,000 - 291,000; Full time

Job summary

Hewlett Packard Enterprise is seeking an AI & Automation Engineer (Contractor) to join our Digital Transformation team in Texas. You will design, build, and productionize enterprise AI/automation solutions, integrating with Databricks, Azure OpenAI, Okta, and other enterprise systems.

The role is hands-on, requiring ownership from requirements through deployment and production support, with collaboration across data engineering, business teams, and IT operations.

Qualifications

• Experience designing and deploying enterprise AI and automation solutions.
• Ability to own development from requirements to production support.
• Strong understanding of ML models and evaluation metrics.
• Experience with cloud services and enterprise integrations.

Responsibilities

• Design and develop enterprise AI, GenAI, ML, and automation solutions.
• Build Python-based applications and services using modern APIs/frameworks.
• Develop AI agents/workflows using LLMs, RAG, and prompts.
• Integrate with Databricks, SailPoint, Okta, and Atlas among others.
• Leverage Azure AI services and Databricks data pipelines for AI use cases.
• Lead testing, deployment across environments, and documentation.

Skills

Python

REST APIs

CI/CD

Generative AI

LLM-based development

Azure OpenAI

Docker

Git

Agent/Tool calling

LangChain

Education

Bachelor's or Master's in Computer Science / IT / Engineering / Data Science

Tools

Databricks

SailPoint

Okta

Azure OpenAI

GitHub / Git

Docker

Azure Container Apps

Job description

Hewlett Packard Enterprise is seeking an AI & Automation Engineer (Contractor) to join our Digital Transformation team in Texas. You will design, build, and productionize enterprise AI/automation solutions, integrating with Databricks, Azure OpenAI, Okta, and other enterprise systems.

The role is hands-on, requiring ownership from requirements through deployment and production support, with collaboration across data engineering, business teams, and IT operations.