AI and Machine Learning Engineer
Company: HP Enterprise
Location: Bengaluru, Karnātaka, India
Type: Full-time
Posted: 2026-07-29
About this role
AI and Machine Learning EngineerThis role has been designed as 'Hybrid' with a requirement 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:
At HPE Networking, the Digital Experience & Automation (DEA) team is reimagining how people experience support and services in a digital‑first world—setting new standards for the future of networking. We enable customers, partners, and employees through AI‑driven tools and modern platforms, transforming support into a unified, efficient, and simple experience that drives measurable value. Our mission is grounded in innovation with purpose: applying automation, AI, and data‑driven insights to simplify journeys, reduce friction, and create meaningful outcomes at every touchpoint.
The AI/ML Engineer designs and builds scalable, production-grade AI/ML solutions for mission-critical cloud applications. This role requires ownership of the complete machine learning lifecycle, including data analysis, feature engineering, model development, deployment, monitoring, and continuous improvement.
The engineer applies advanced technical expertise to architect, prototype, and implement AI/ML and GenAI-driven cloud solutions using modern MLOps practices for reliable, reproducible, and secure depl...