Finance Transformation Data/AIML Engineer

Hewlett Packard Enterprise•Spring, TX

9d•$126,500 - $290,500•Onsite

About The Position

Hewlett Packard Enterprise is seeking an AI and Automation Engineer for its Digital Transformation / Automation team. This hands-on engineering role supports the design, development, integration, testing, deployment, and production support of enterprise AI and automation solutions. The engineer works with the Lead Automation Architect, business teams, data engineering teams, and enterprise technology teams to translate business requirements into scalable, secure, production-ready solutions. The internal team lead retains ownership of the solution roadmap, architecture approvals, prioritization, and business stakeholder relationships. The role includes developing AI, generative AI, agentic AI, machine-learning, and automation solutions; building Python applications and services; creating LLM-powered agents and workflows; and integrating with Azure services, enterprise data, and business platforms. It also involves advanced research and solution architecture for complex AI problems, technical leadership and mentorship, design reviews, cross-functional collaboration, continuous improvement, and communicating technical findings to stakeholders. The position may also involve reporting data findings and maintaining or updating business intelligence tools, databases, dashboards, systems, or methods. The position is designated onsite, with the expectation that the employee will primarily work from an HPE office. HPE describes its culture as inclusive and offers programs supporting employee wellbeing and professional development.

Requirements

At least 5 years of overall software engineering, automation, data, or AI development experience.
At least 3 years of strong Python development experience.
Strong hands-on experience with Python.
Strong understanding of REST APIs and system integrations.
Experience developing enterprise applications or automation solutions and deploying solutions to cloud servers.
Experience with Git/GitHub or equivalent source-control tools.
Experience with CI/CD and production deployment practices.
Hands-on experience developing generative AI or LLM-based applications.
Experience with prompt engineering and structured LLM outputs, RAG and enterprise knowledge retrieval, and agent/tool/function calling concepts.
Experience with LLM evaluation, guardrails, error handling, and hallucination mitigation.
Experience with Azure OpenAI or comparable enterprise LLM platforms and Microsoft Azure.
Deep understanding of machine-learning algorithms, model selection, hyperparameter tuning, and performance evaluation using appropriate metrics.
Strong foundation in mathematics and statistics, including linear algebra, calculus, probability theory, and statistical concepts.
Proficiency in programming languages such as Python, R, or Java.
Experience producing production-level code and familiarity with software-engineering best practices, version control, and software-development methodologies.
Advanced knowledge and experience in deep learning, including neural-network architectures and techniques such as transfer learning, generative models, and optimization algorithms.
Research skills and ability to stay current with AI and machine-learning advancements and assess emerging technologies.
Ability to provide technical leadership and mentorship to junior team members.
Excellent communication and collaboration skills for working with cross-functional teams and stakeholders.
Strong problem-solving and critical-thinking abilities.
Bachelor’s or master’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline, or equivalent strong industry experience.

Nice To Haves

Knowledge of libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras.
Recent hands-on exposure to generative AI/LLM-based application development is strongly preferred.
Experience with agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent.
Experience with Azure Container Apps, App Service, or Functions.
Experience with Docker and container-based application deployment.

Responsibilities

Design and develop enterprise AI, generative AI, agentic AI, machine-learning, and automation solutions.
Build applications and services using Python and modern API and framework technologies.
Develop AI agents and workflows using LLMs, prompt engineering, tool or function calling, RAG, and enterprise data sources.
Integrate solutions with enterprise applications and platforms, including Databricks, SailPoint, Okta, Salesforce, SAP, Anaplan, Power BI, and ServiceNow, where required.
Develop and integrate solutions using Azure services, including Azure OpenAI and related cloud services.
Work with Databricks datasets and data pipelines for AI/ML and analytical use cases; apply knowledge of ML-based forecasting and different ML models.
Implement authentication and authorization using enterprise identity solutions such as Okta, Entra ID / Azure AD, OAuth, and role-based access controls.
Perform unit, integration, and regression testing, and support business UAT.
Support deployment across Development, Test/UAT, and Production environments.
Troubleshoot application, integration, infrastructure, and data issues.
Follow enterprise standards for security, logging, monitoring, exception handling, source control, and deployment.
Prepare technical documentation, including solution designs, deployment instructions, support documentation, and test evidence.
Provide progress updates and communicate risks, dependencies, and blockers.
Conduct advanced research into AI and machine learning, track emerging technologies, and identify opportunities to apply new techniques to business problems.
Design and architect AI solutions by analyzing business requirements and constraints, selecting appropriate models and algorithms, and considering scalability, performance, and maintainability.
Provide technical guidance and mentorship to junior team members, share best practices, and review code and designs.
Collaborate with product managers, data scientists, business analysts, stakeholders, and cross-functional teams to translate requirements into technical solutions and support successful implementation.
Drive continuous improvement and innovation in AI and machine-learning practices, including evaluating and integrating third-party tools or services.
Facilitate project design reviews and mentor junior team members during review sessions.
Collaborate with the engineering manager and team lead to refine design and implementation strategies and provide constructive feedback to peers.
Participate in and coordinate meetings to support communication and coordination among team members.
Prepare and deliver detailed presentations and reports, explaining technical concepts to non-technical audiences.
As applicable, interpret and report data findings; maintain or update business intelligence tools, databases, dashboards, systems, or methods; and contribute to solutions for complex application, system administration, or network concerns.