Finance Transformation Data/AIML Engineer

Finance Transformation Data/AIML Engineer

This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.

Who We Are:

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Job Description:

We are looking for an AI & Automation Engineer (Contractor) to join our Digital Transformation / Automation team and support the design, development, integration, and production deployment of enterprise AI and automation solutions.

The role will work closely with the Lead Automation Architect, business teams, data engineering teams, and enterprise technology teams to convert business requirements into scalable, secure, and production-ready solutions.

This is a hands-on engineering role. The ideal candidate should be comfortable owning development from requirements and technical design through testing, deployment, and production support.

Selected candidates will work under the direction of the internal team lead, who retains ownership of the solution roadmap, architecture approvals, prioritisation, and business stakeholder relationships.

Key Responsibilities

• •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.

Knowledge and Skills:

• •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.

Required Technical Skills

• •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