HPE
Finance Transformation Data/AIML Engineer
$126K–$290K
Spring, Texas, United States of America · Full-time · Posted Oct 6, 2026
- Experience
- 5+ years of overall software engineering / automation / data / AI development experience; minimum 3+ years of strong Python development experience. Recent hands-on GenAI/LLM application development is strongly preferred.
- Education
- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline; equivalent strong industry experience may also be considered.
About the job
Build, integrate, deploy, and support enterprise AI, machine-learning, and automation solutions for finance transformation.
What you'll do
- Develop AI/GenAI applications, agents, and workflows, and integrate them with enterprise platforms and data sources.
- Test, deploy, troubleshoot, and support solutions across development, test/UAT, and production environments.
- Research AI/ML advances, design scalable solutions, and provide technical guidance to junior team members and stakeholders.
What they ask for
- Strong hands-on Python experience and strong understanding of REST APIs and system integrations.
- Experience developing enterprise applications or automation solutions, including cloud deployment; experience with Git or equivalent source control and CI/CD.
- Hands-on Generative AI/LLM application development, including prompt engineering, RAG, agent/tool calling, evaluation, guardrails, and hallucination mitigation.
- Experience with Microsoft Azure and Azure OpenAI or comparable enterprise LLM platforms.
Nice to have
- Agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, or AutoGen, or equivalent.
- Libraries and frameworks such as TensorFlow, PyTorch, sci-kit, and Keras.
Pay
USD 126,500 - 290,500 (base salary)
Tools
Python, REST APIs, Git / GitHub, Microsoft Azure, Azure OpenAI, Databricks, Docker, LangChain
AI focus
Hands-on enterprise AI, GenAI, agentic AI, and machine-learning engineering, including LLM agents and workflows, RAG, forecasting, model design, evaluation, and production deployment.
Model-written summary · the original posting is authoritative