Finance Transformation Data/AIML Engineer

Hewlett Packard Enterprise • Spring, TX

12d • $126,500 - $290,500 • Onsite

About The Position

Hewlett Packard Enterprise is seeking an AI & Automation Engineer (Contractor) for its Digital Transformation / Automation team. The role supports the design, development, integration, testing, deployment, and production support of secure, scalable 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 production-ready solutions. The internal team lead retains ownership of the solution roadmap, architecture approvals, prioritisation, and business stakeholder relationships.

The position involves hands-on development of AI, generative AI, agentic AI, machine learning, and automation solutions using Python, APIs, Azure services, Databricks, LLMs, and enterprise systems. The engineer is also expected to conduct advanced AI and machine learning research, architect solutions for complex problems, assess scalability and maintainability, provide technical guidance to junior team members, collaborate with cross-functional stakeholders, and support continuous improvement and innovation.

The role is designed as onsite, with an expectation that the employee primarily works from an HPE office. The listed US base salary range for Texas is USD 126,500–290,500 annually; variable incentives may also be offered. HPE describes its benefits as supporting physical, financial, and emotional wellbeing and offers programs for personal 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, including deploying solutions to cloud servers.
• Experience with Git, GitHub, or equivalent source-control systems.
• Experience with CI/CD and production deployment practices.
• Hands-on experience developing generative AI / LLM-based applications.
• Experience with prompt engineering and structured LLM outputs.
• Experience with RAG and enterprise knowledge retrieval.
• Understanding of agent, tool, and 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.
• 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.
• Deep understanding of machine learning algorithms, model selection, hyperparameter tuning, and model-performance evaluation using appropriate metrics.
• Strong foundation in mathematics and statistics, including linear algebra, calculus, probability theory, and statistical concepts.
• Proficiency in Python, R, or Java; production-level coding experience; and familiarity with software engineering best practices 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.
• Ability to conduct research, explore emerging technologies, and identify opportunities to apply state-of-the-art techniques to complex problems.
• Technical leadership, mentorship, and guidance skills, along with strong communication, collaboration, problem-solving, and critical-thinking abilities.
• 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.

Nice To Haves

• Recent hands-on exposure to generative AI / LLM-based application development is strongly preferred.
• Knowledge of libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras.

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/function calling, RAG, and enterprise data sources.
• Integrate solutions with enterprise applications and platforms, including Databricks, SailPoint, Okta, Atlas, Salesforce, SAP, Anaplan, Power BI, and ServiceNow, as 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, including 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.
• Work independently and provide clear progress updates, risks, dependencies, and blockers.
• Conduct advanced research in AI and machine learning, stay current with developments, explore emerging technologies, and identify opportunities to apply new techniques to business problems.
• Analyze business requirements and constraints and design appropriate AI solutions, machine learning models, and algorithms for complex problems.
• Consider scalability, performance, and maintainability when designing solutions.
• Provide technical guidance and mentorship to junior team members, share best practices, review code and designs, and help resolve technical challenges.
• 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 and promote adoption of best practices.
• Evaluate and integrate third-party tools or services that can enhance AI solutions.
• 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 effective team communication and coordination.
• Prepare and deliver detailed presentations and reports, explaining technical concepts to non-technical audiences.
• Where applicable, interpret and report data findings and maintain or update business intelligence tools, databases, dashboards, systems, or methods.
• Where applicable, help design and develop solutions to complex application problems, system administration issues, or network concerns.

Benefits

• A comprehensive suite of benefits intended to support physical, financial, and emotional wellbeing for team members and their loved ones.
• Personal and professional development programs to help employees pursue career goals.

What This Job Offers

• Career Level: Senior