Finance Transformation Data/AIML Engineer

• Design and develop enterprise AI, GenAI, Agentic AI, ML, and automation solutions.
• Build applications and services using Python and modern API/framework technologies.
• Develop AI agents and workflows using LLMs, prompt engineering, tool/function calling, RAG, and enterprise data sources.
• Integrate solutions with enterprise applications like Databricks, SailPoint, Okta, Atlas etc.
• 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. Having knowledge of ML based forecasting and different ML Models
• Support integration with enterprise platforms such as Salesforce, SAP, Anaplan, Power BI, ServiceNow, and other business systems, where required.
• Implement authentication and authorization using enterprise identity solutions such as Okta, Entra ID / Azure AD, OAuth, and role-based access controls.
• Perform unit testing, integration testing, 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 required technical documentation including solution design, deployment instructions, support documentation, and test evidence.
• Work independently while providing clear progress updates, risks, dependencies, and blockers.
• Responsible for conducting advanced research in AI and machine learning. This includes staying up to date with the latest advancements in the field, exploring emerging technologies, and identifying opportunities to apply cutting-edge techniques to solve complex business problems.
• Tasked with designing and architecting AI solutions for complex problems. This involves analyzing business requirements, understanding constraints, and proposing appropriate machine learning models and algorithms.
• Responsible for considering scalability, performance, and maintainability while designing the solution.
• Provides technical guidance and mentorship to junior team members. This includes sharing best practices, reviewing code and designs, and helping team members overcome technical challenges. Participate in technical discussions and provide thought leadership within the organization.
• Works closely with stakeholders, such as product managers, data scientists, and business analysts, to understand their requirements and translate them into technical solutions. Collaborate with cross-functional teams to ensure alignment and successful AI and machine learning project implementation.
• Responsible for driving continuous improvement and innovation in the organization's AI and machine learning practices. This involves identifying areas of improvement, exploring new techniques or technologies, and promoting the adoption of best practices.
• Be involved in evaluating and integrating third-party tools or services that can enhance the capabilities of AI solutions.
• Facilitates design review sessions for your projects, ensuring alignment with project requirements and best practices.
• Mentor junior team members during review sessions.
• Collaborates closely with the engineering manager and team lead to refine and iterating on design and implementation strategies, providing constructive feedback to peers.
• Participates in and coordinates meetings, ensuring effective coordination and communication among team members.
• Independently prepares and delivers detailed presentations and reports to stakeholders, translating complex technical concepts into understandable terms for non-technical audiences.
• May be required to interpret and report data findings and maintain or update specific business intelligence tools, databases, dashboards, systems, or methods
• May be involved in the design and development of solutions to complex application problems, system administration issues, or network concerns, where applicable to the role.

• Deep understanding of machine learning algorithms, such as linear regression, decision trees, support vector machines, random forests, deep learning models (e.g., neural networks), and reinforcement learning. Proficient in model selection, hyperparameter tuning, and evaluating model performance using appropriate metrics.
• A strong foundation in mathematics and statistics. In-depth knowledge of linear algebra, calculus, probability theory, and statistical concepts. Understanding and developing complex machine learning models and algorithms.
• Proficiency in programming languages such as Python, R, or Java is expected. Experience developing production-level code and familiarity with software engineering best practices, version control systems (e.g., Git), and software development methodologies are also required. Additionally, knowledge of libraries and frameworks like TensorFlow, PyTorch, sci-kit, and Keras is a plus.
• Advanced knowledge and experience in deep learning. Understanding advanced neural network architectures (e.g., convolutional neural networks, recurrent neural networks, transformers) and advanced techniques such as transfer learning, generative models, and optimization algorithms for deep learning.
• Actively staying updated with the latest AI and machine learning research advancements. Experience conducting research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques to solve complex problems.
• Provide technical leadership, mentorship, and guidance to junior team members. Must have excellent communication skills to collaborate with cross-functional teams and stakeholders effectively. Possess strong problem-solving and critical thinking abilities to guide projects, make strategic decisions, and solve complex technical challenges.

• Strong hands-on experience with Python.
• Strong understanding of REST APIs and system integrations.
• Experience developing enterprise applications or automation solutions including deployment of solutions to cloud servers
• Git / GitHub or equivalent source-control experience.
• Experience with CI/CD and production deployment practices.
• Hands-on experience with Generative AI / LLM-based application development.
• Prompt engineering and structured LLM outputs.
• RAG and enterprise knowledge retrieval.
• Agent / tool / function calling concepts.
• LLM evaluation, guardrails, error handling, and hallucination mitigation.
• Experience with Azure OpenAI or comparable enterprise LLM platforms.
• Experience working with Microsoft Azure.
• Agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent.
• Azure Container Apps / App Service / Functions.
• Docker and container-based application deployment.

• 5+ years of overall software engineering / automation / data / AI development experience.
• Minimum 3+ years of strong Python development experience.
• Demonstrable hands-on experience delivering enterprise applications or automation solutions.
• Recent hands-on exposure to GenAI/LLM-based application development strongly preferred.

• 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.

Accessibility

HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here .

Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.