Principal Engineer - AI Architect

Principal Engineer - AI Architect

This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week 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 seeking an AI Architect a senior technical leader responsible for designing and overseeing end-to-end AI systems that align with business objectives. This role bridges business strategy and technical execution, ensuring scalability, security, and compliance across AI solutions. Should have Good Networking knowledge

Responsibilities:

Design end-to-end AI systems (data ingestion → model deployment → UI).
Collaborate with AI/ML engineers, Data engineers, and MLOps for production readiness.

Strategic Planning & Business Alignment

• Define AI roadmap aligned with organizational goals.
• Identify high-impact AI use cases and assess ROI.
• Engage stakeholders early for feasibility and governance.

Architecture & System Design

• Design AI system architecture (data pipelines, model lifecycle, orchestration).
• Integrate AI with enterprise apps and internal systems.
• Ensure scalability, fault tolerance, and cost efficiency.

Model & Data Management

• Select appropriate ML/DL models and frameworks (TensorFlow, PyTorch).
• Define versioning, fine-tuning, and deployment strategies.
• Implement CI/CD for ML pipelines and monitoring.

Security, Compliance & Governance

• Enforce data privacy, ethical AI practices, and audit trails.
• Implement guardrails for LLMs and prompt safety.
• Collaborate with security teams for risk mitigation.

Cross-Functional Leadership

• Mentor engineers and data scientists.
• Translate business requirements into technical tasks.
• Drive collaboration across product, data, and operations teams.