Distinguished Technologist, Agentic AIOps & Self-Driving Networks

Distinguished Technologist, Agentic AIOps & Self-Driving Networks

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:

Key responsibilities


* Own the E2E assurance architecture: cross-domain telemetry normalization, correlation, root cause, guarded remediation, and outcome verification.


* Define the shared substrate - a common model spanning intent, topology, config, identity, policy, and state


* Architect the agentic layer: planning and tool-use agents, MCP tool servers, and multi-agent orchestration across domain boundaries.


* Own the safety architecture: approval gates, policy enforcement at tool boundaries, remediation safety tiers, blast-radius limits, and deterministic rollback.


* Set the discipline for when to use deterministic graph reasoning, classical ML, or generative AI - and when not to.


* Establish evaluation as a shipped discipline - golden fault corpora, replayable incident harnesses, and MTTR and false-escalation rate as product metrics.


* Carry the strategy across BUs and influence executive and product stakeholders toward one substrate.


* Mentor engineers and develop the next generation of principal and distinguished technical leaders.

Requirements


* 15+ years in engineering and architectural leadership building and operating large distributed systems in production.


* Breadth across networking domains - campus, data center, WAN/SD-WAN, and security - with proven depth in at least three.


* Deep networking fundamentals: EVPN-VXLAN, BGP, SAI-level behavior, overlay/underlay, identity and policy enforcement, and streaming telemetry (gNMI/OpenConfig) at scale.


* Graph systems depth: knowledge or property graph modeling and graph algorithms applied to dependency, impact, and causality.


* Applied AI on both sides: LLM agents, tool calling, MCP, and agent evaluation, plus classical ML and time-series modeling - and the judgment to know which the problem calls for.


* Production AIOps or observability experience: correlation, anomaly detection, noise reduction, incident lifecycle.


* Cloud-native at scale: Kubernetes, microservices, Kafka, time-series/OLAP stores, hybrid and public cloud.
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