Description
We are seeking a Distinguished Technologist to lead the development of Agentic AIOps and Self-Driving Networks. As a Distinguished Technologist at Hewlett Packard Enterprise, you will own the E2E assurance architecture, define the shared substrate, architect the agentic layer, own the safety architecture, set the discipline for AI and ML usage, establish evaluation as a shipped discipline, carry the strategy across business units, and mentor engineers.
Key Responsibilities:
- Own the E2E assurance architecture: cross-domain telemetry normalization, correlation, root cause analysis, 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.
- Strong hands-on coding in Python and Go, sustained through design and architecture. Proficient with AI-first, spec-driven prototyping.
- Demonstrated impact at Principal or Distinguished level spanning multiple organizations, including the ability to run discovery across teams that don't report to you.
- Bachelor's, Master's, or PhD in computer science, engineering, or a related discipline preferred.
Benefits:
- Comprehensive suite of benefits supporting physical, financial, and emotional wellbeing.
- Investment in career development and growth.
- Unconditional inclusion and flexible work arrangements.