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:
HPE Financial services is where we help organizations create the investment they need for digital transformation, in an innovative and sustainable way. We partner with customers across their entire IT asset portfolio from edge to cloud to end-user. Unique to each client’s aspirations and size, our financial and asset management solutions are anchored by best-in-class tech upcycling services. Join us redefine what’s next for you.
Role Summary
The Senior AI & Data Engineer is an individual contributor role that acts as the technical subject matter expert at the intersection of AI engineering and data engineering. This is a uniquely dual-domain role: the successful candidate bridges the organization’s data strategy with its AI agenda, ensuring that intelligent systems are built on a foundation of governed, high-quality, well-architected data. On the AI side, this role designs and delivers end-to-end production-grade AI products — architecting multi-agent frameworks, fine-tuning and evaluating LLMs, building robust ML pipelines, and ensuring AI solutions are scalable, explainable, and responsibly governed. On the data side, this role defines the data architecture, transformation standards, and quality frameworks that make those AI products possible — owning the data platform across Databricks, Microsoft Fabric, and Collibra. Beyond individual delivery, this role provides technical leadership across cross-functional initiatives, defines engineering standards for both AI and data engineering practice, and leads the Applied AI Engineer and AI Data Engineer. The ideal candidate brings deep expertise across the full stack: from classical machine learning and modern generative AI through to data architecture, governance, and pipeline engineering — with the communication skills to translate that expertise into measurable business impact.
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- Serve as the dual AI & data SME for the team and organization — the escalation point for complex technical decisions spanning model design, data architecture, pipeline engineering, and deployment.
- Define and uphold engineering standards, design patterns, and best practices across both AI and data engineering disciplines; conduct architecture reviews and provide technical sign-off on all major deliverables.
- Lead technical discovery for new AI and data use cases: evaluate feasibility, recommend solution approaches, and produce architecture documents for stakeholder alignment.
- Participate in and lead cross-functional initiatives where AI and data strategy intersect — partnering with product, business, and platform teams to translate strategy into shipped solutions.
- Mentor and upskill the Applied AI Engineer and AI Data Engineer through pair programming, code reviews, design sessions, and structured knowledge transfer.
Advanced LLM Engineering & Agentic AI
- Architect and deliver complex agentic AI systems — multi-agent pipelines, tool-use frameworks, autonomous task orchestration
- Design and implement advanced RAG architectures including hybrid search, re-ranking, query decomposition, and self-reflective retrieval patterns
- Lead LLM evaluation frameworks: define metrics, build automated eval harnesses, and benchmark Claude, GPT, and Copilot performance against business KPIs
- Assess and implement LLM fine-tuning and alignment strategies where pre-trained models do not meet requirements
- Own LLM integration architecture — API design, latency optimization, token cost management, rate limit handling, and fallback strategies
Machine Learning & Data Science
- Lead the full ML lifecycle: problem framing, data strategy, feature engineering, model selection, training, evaluation, deployment, and monitoring.
- Develop advanced ML solutions across NLP, time-series forecasting, anomaly detection, recommendation, and classification/regression domains.
- Design and implement MLOps pipelines for automated model training, versioning, A/B testing, and drift detection in production environments.
- Apply statistical rigour — hypothesis testing, causal inference, experimental design — to validate model outcomes and business impact.
- Ensure explainability (SHAP, LIME, attention visualization) and fairness assessments are embedded in all production models.
AI Solution Architecture & Integration
- Architect end-to-end AI solutions that integrate LLMs, ML models, vector stores, data pipelines, and business APIs into cohesive, production-ready products.
- Define data contracts and interface specifications between the AI engineering layer and the data engineering team.
- Design for scale, reliability, and cost — including caching strategies, async processing, streaming inference, and model serving optimization.
- Evaluate and recommend AI frameworks, platforms, and tooling to the technical leadership team; maintain a technology radar for the AI practice.
Data Engineering, Governance & Transformation
- Architect data transformations and ingestion methods for AI products
- Define and enforce data engineering standards, pipeline design patterns, and transformation best practices; ensure the AI Data Engineer delivers within a governed, reusable framework.
- Design advanced transformation patterns: SCD handling, event-driven streaming ingestion, late-arriving data, and incremental load strategies optimized for both analytical and AI workloads.
- Implement automated data quality validation
- Champion a data-as-a-product mindset
RPA, Reporting & AI Automation
- Provide technical leadership for AI-augmented RPA implementations — embedding LLM-based intelligence into automation workflows to handle unstructured inputs and exception scenarios.
- Define AI metrics and KPIs for dashboards; support Power BI and reporting teams with data science-derived insights and model output integration.
- Identify automation opportunities across the organization and build the business case for AI-led process transformation.
Innovation & Thought Leadership
- Develop quick PoC's to
- Continuously evaluate emerging research, models, and frameworks; translate relevant advances into internal prototypes and proof-of-concepts.
- Present technical findings and recommendations to senior stakeholders and leadership — with clarity, confidence, and business context.
- Contribute to internal AI community of practice: run knowledge sessions, publish internal technical guides, and foster a culture of experimentation
What you need to bring:
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Engineering, Mathematics, or a related technical discipline; PhD is a plus.
- 7 – 10 years of hands-on experience in AI/ML engineering, applied data science, or LLM engineering roles.
- Proven track record of delivering production AI systems — not just prototypes — with measurable business impact.
- Deep expertise with at least two major LLM platforms (Claude, GPT, Gemini, or equivalent), including evaluation and integration at scale.
- Significant experience with Collibra or an
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