LLM Engineer

• lead the product marketing strategy for Cisco’s AI infrastructure portfolio
• connect technical capabilities to the business outcomes customers require for AI workloads and AI-native applications
• define the narrative, positioning, messaging, launch strategy, and sales enablement required to power secure, connected, AI-ready organizations
• partner with Product Management, Engineering, Sales, Corporate Marketing, Analyst Relations, and Communications
• translate sophisticated infrastructure innovation into market value
• drive cross-functional alignment
• equip the business to win with enterprise buyers and executives

Director of Product Marketing Strategy – AI Infrastructure

• Own and drive the global 4P strategy for ACS AI infrastructure platforms, from annual planning through execution and performance tracking.
• Define clear value propositions, positioning, and segmentation for AI infrastructure solutions aligned to customer workloads and industry use cases.
• Drive execution of the AI GTM plan with regions and countries, ensuring timely rollout and consistency.
• Act as the WW GTM expert for ACS AI infrastructure solutions (e.g., ZGX Nano, ZGX Fury, Boost), ensuring tight alignment between product capabilities and market needs.
• Translate complex AI infrastructure architectures into compelling, sales‑ready GTM narratives and offers.
• Partner with GBU and engineering teams to influence roadmap priorities based on market and customer feedback.
• Support priority deals, lighthouse customers, and strategic pursuits requiring AI infrastructure expertise.
• Enable sales teams with clear guidance, playbooks, and tools to accelerate AI‑led pipeline generation and conversion.
• Collaborate closely with alliance teams (e.g., NVIDIA, AMD, Intel, ISVs) to deliver joint GTM programs, aligned launches, and co‑selling motions tied to AI infrastructure platforms.
• Define and track KPIs to measure GTM effectiveness, pipeline impact, and revenue outcomes.

Global GTM & 4P Category Manager – AI Infrastructure

• Design and build the distributed, fault-tolerant infrastructure that powers Anrok's AI agent capabilities.
• Own technical architecture decisions for new agent infrastructure services end-to-end, from initial design through production deployment and iteration.
• Build the evaluation, observability, and reliability tooling that lets the rest of the team ship agents confidently on top of your infrastructure.
• Collaborate closely with product engineers and our tax data team to translate real compliance workflows into reliable, scalable agent primitives.
• Drive engineering best practices across the team: code quality, testing discipline, system design, and operational health.
• Identify and fix performance, reliability, and scalability bottlenecks as our agent systems grow.
• Help grow the team—mentoring engineers, contributing to technical culture, and raising the bar on how we build.

Software Engineer, Agentic AI Infrastructure

• Own the discovery and definition of customer requirements for AI infrastructure use cases, including training, inference, GPU clusters, bare metal, managed orchestration, networking, and storage
• Work directly with strategic customers to understand their technical needs, deployment timelines, workload patterns, and success criteria
• Translate customer requirements into clear product requirements, technical specifications, and engineering priorities
• Partner closely with engineering, infrastructure, networking, storage, and operations teams to deliver customer-ready solutions
• Drive alignment across customer needs, product roadmap, architecture decisions, and delivery execution
• Understand and define requirements across GPU compute, CPU, memory, local and shared storage, high-performance networking, cluster topology, and orchestration layers such as Kubernetes and Slurm
• Support customer conversations around infrastructure design, capacity planning, performance expectations, operational readiness, and acceptance criteria
• Help define product capabilities that can scale beyond a single customer into repeatable AI infrastructure offerings

Senior Product Manager, AI Infrastructure

• Lead architecture for DPU-accelerated host networking, security, and network services.
• Define what runs on the DPU versus the host CPU, including tradeoffs around performance, cost, failure isolation, observability, debuggability, and operational complexity.
• Architect distributed firewalling, micro segmentation, virtual routing, service chaining, traffic steering, and line-rate policy enforcement.
• Guide platform decisions across DPU SDKs, programmable pipelines, Linux networking, control-plane services, telemetry, policy distribution, and upgrade models.
• Design APIs, policy models, configuration flows, state synchronization mechanisms, and recovery behavior for distributed networking systems.
• Build static-stability and failure-mode thinking into the architecture so the system behaves predictably during partial failures, control-plane loss, upgrades, rollbacks, and fleet-wide rollout.
• Provide technical leadership for AI cluster networking challenges, including congestion collapse, incast, ECMP imbalance, PFC/ECN behavior, noisy tenants, NIC firmware issues, switch misconfiguration, and GPU underutilization caused by network stalls.
• Align cross-functional stakeholders across hardware, firmware, product, and customer-facing teams to successfully translate complex architectural visions into shippable software.
• Elevate engineering standards across the organization through meticulous design reviews, proactively identifying systemic risks to scale, reliability, and security.
• Mentor senior engineering talent, accelerating team growth and technical depth in packet processing, host networking, and control-plane design.

Technical Leader, DPU Networking, AI Infrastructure

• Design end-to-end AI solutions on Dataiku's platform, leveraging Dataiku Agent Hub, Prompt Studio, LLM Mesh, and Knowledge Banks (Vector Stores), or Python-based frameworks where needed.
• Build and orchestrate multi-agent systems using Dataiku's Visual Agents (simple and structured), as well as code-based frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK) as appropriate.
• Integrate and optimize LLM APIs across providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure, open-source models via Dataiku's LLM Mesh), applying model routing strategies to balance cost, latency, and quality.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including agentic RAG and GraphRAG, using Dataiku's Knowledge Banks with reranking, dynamic filtering, and document extraction capabilities.
• Work exclusively with the Marketing organisation, partnering across functions such as Demand Generation, Content Marketing, Product Marketing, Field Marketing, Marketing Operations, Brand, and Communications.
• Engage marketing stakeholders to gather business requirements, then go further: identify the underlying user or team pain points those requirements represent, and design solutions that address both the stated need and the deeper problem.
• Own projects end-to-end, from requirements intake and solution design through to build, deployment, and handover.
• Develop autonomous and semi-autonomous AI agents, using Dataiku's Agent Builder, custom Python-based architectures (LangGraph, CrewAI, Claude Agent SDK, etc.), or a combination of both. Exercise judgment on when to leverage platform capabilities and when to build custom solutions.
• Design and build Agent Tools beyond documented examples, including custom API integrations, data retrieval modules, decisioning logic, and automated workflows, pushing past out-of-the-box patterns to deliver solutions tailored to specific business problems.
• Build, publish, and consume MCP (Model Context Protocol) servers to enable agent-to-tool integration across systems, including designing custom MCP servers where needed.
• Develop evaluation and monitoring approaches for agent systems, combining Dataiku's built-in capabilities with custom instrumentation to measure reliability, accuracy, cost, and business impact in production.
• Design and maintain evaluation frameworks (evals) for LLM-based systems, measuring accuracy, latency, cost, and reliability in production.
• Adhere to data governance, security, and regulatory compliance requirements (EU AI Act awareness, responsible AI practices) for all AI solutions.
• Leverage Dataiku's Cost Guard and Quality Guard features to manage LLM spend, enforce usage policies, and maintain output quality standards.
• Work closely with analytics and data engineering teams to maintain metadata on reference datasets for LLM consumption.
• Create front-end user interfaces for AI applications using HTML, CSS, and JavaScript, within Dataiku's webapps framework, Dataiku Answers for chat-based interfaces, or standalone applications built with Vue.js and Node.js.
• Collaborate on UX design, ensuring internal stakeholders find AI solutions intuitive and responsive.
• Provide product feedback to the development team to improve the platform.
• Stay current with the rapidly evolving AI engineering landscape, agent frameworks, model capabilities, evaluation practices, governance requirements, and tools like MCP and A2A protocols.

Generative AI Engineer

• Lead bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads, setting the standard for how the team operates.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Profile and optimize end-to-end workload performance across compute, memory, networking, and communication layers using tools such as Nsight Systems, NCCL tests, and custom microbenchmarks.
• Analyze scaling efficiency for distributed LLM workloads using data, tensor, pipeline, and expert parallelism across modern GPU clusters, and translate findings into concrete tuning guidance.
• Own root-cause analysis of complex failures — hangs, performance regressions, topology sensitivity in large distributed environments.
• Define and build the resilience and failure-attribution stack: detecting, triaging, and attributing node, fabric, and workload failures across the cluster at scale.
• Build repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.
• Mentor engineers, drive technical standards, and act as a force multiplier across the broader performance and infrastructure organization.

Senior Software Engineer, DGX Cloud AI Infrastructure

• Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Perform root-cause analysis of failures in large distributed environments.
• Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster.
• Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.

Software Engineer, DGX Cloud AI Infrastructure

• architect and scale foundational platforms that enable AI-driven silicon development across GPU and system-level design teams.
• impact how efficiently large-scale simulation, design workflows, and machine learning workloads are executed.
• lead the development of distributed, data-intensive infrastructure and data platforms that integrate directly with engineering workflows.
• drive cross-functional collaboration with hardware, software, and system teams to integrate AI capabilities.
• accelerate scalable model training, data processing, and deployment within the silicon development lifecycle.
• architect and deploy scalable AI/HPC infrastructure (on-prem, cloud, or hybrid) optimized for EDA workflows.
• develop and operationalize MLOps pipelines for model training, validation, and deployment integrated with semiconductor design and verification flows.
• build and manage large-scale data pipelines to ingest, process, and serve engineering datasets.

Senior Staff Engineer, AI Infrastructure

• Design, develop and maintain Generative AI applications, advanced AI models and conversational agents.
• Use Gen AI techniques like Retrieval Augmented Generation (RAG), Prompt Engineering, Embeddings etc. to enhance model outcomes.
• Perform evaluation on LLM output using appropriate metrics and iteratively improve results.
• Stay updated with the latest industry trends and advancements in AI to implement cutting-edge solutions.
• Communicate model results and solicit feedback from stakeholders at various levels.

Generative AI Engineer – Internship

• Ship production GenAI features that improve customer workflows
• Build reliable patterns for LLM integration, tool use, evaluation, and observability
• Improve the accuracy, latency, cost, and trustworthiness of AI-enabled systems
• Help teams move from prototypes to maintainable production services
• Partner across engineering, product, data, security, and business teams
• Contribute to responsible AI practices in an enterprise software environment

• Assist in creating and updating AutoCAD drawings, rack elevations, cabinet layouts, and structured cabling documentation.
• Support data hall design documentation, asset tracking, and revision management activities.
• Assist engineering teams with infrastructure inventory validation and basic capacity tracking.
• Help maintain design standards, templates, and project documentation repositories.
• Participate in engineering reviews, design walkthroughs, and quality assurance activities.
• Coordinate with cross-functional teams to gather project inputs and update design records.
• Support preparation of project reports, spreadsheets, diagrams, and technical documentation.
• Learn data center infrastructure concepts including power, cooling, cabling, rack configurations, and AI cluster environments.
• Follow established operational procedures, engineering standards, and safety requirements.
• Assist with administrative and project coordination tasks related to infrastructure deployment activities.

AI Infrastructure DC Design Intern

• Lead large-scale AI infrastructure deployment programs across multiple sites, regions, or business units.
• Drive end-to-end project execution for GPU clusters, AI compute environments, storage platforms, high-speed networks, and data center infrastructure.
• Develop integrated project plans, implementation strategies, and operational readiness frameworks.
• Manage cross-functional coordination between engineering, operations, supply chain, vendors, and executive stakeholders.
• Identify and mitigate program risks, schedule impacts, technical dependencies, and operational constraints.
• Lead infrastructure migration, expansion, upgrade, and modernization initiatives.
• Drive governance reviews, project reporting, KPI tracking, and executive-level communications.
• Coordinate infrastructure acceptance testing, deployment validation, and production readiness activities.
• Mentor junior project managers and contribute to PMO process standardization and operational maturity.
• Support vendor negotiations, technical evaluations, and infrastructure planning initiatives.

AI Infrastructure TPM II

• Define and evolve the long-term AI/ML research strategy and technical roadmap for Trase OS in alignment with product and platform direction.
• Lead large-scale experimentation and prototyping efforts requiring significant compute infrastructure, translating frontier AI research into scalable, production-grade systems with measurable impact.
• Drive original research and technical breakthroughs in agentic systems, autonomous execution, multi-agent orchestration, post-training and fine-tuning systems, SLM/LLM-based architectures, and applied AI infrastructure.
• Design how models operate within long-lived execution environments, including agent workflows, tool use, planning, memory systems, reasoning, and human-in-the-loop controls.
• Establish evaluation methodologies and reliability frameworks for autonomous systems, including benchmarking, regression testing, safety, controllability, and production behavior analysis.
• Drive architecture decisions across orchestration, model serving, routing, inference, and infrastructure governance, including latency, reliability, and cost optimization.
• Partner closely with engineering and product teams to operationalize research outcomes into deployable systems and enterprise workflows.
• Build AI systems that operate reliably in regulated and constrained environments, including secure cloud, on-premise, and air-gapped deployments.
• Contribute to the broader AI research community through technical papers, publications, conference participation, architecture proposals, and thought leadership.
• Serve as a senior technical authority and mentor across the organization, influencing technical direction, research rigor, experimentation practices, and best practices across research, engineering, and product teams.

Principal AI Researcher – Agentic Systems, AI Infrastructure

• Identify, vet, and manage Tier 1/2 OEMs and regional distributors for high-density servers, network gear, and cabling.
• Drive end-to-end contract lifecycles, including Master Purchase Agreements (MPAs), Service Level Agreements (SLAs), and complex warranty/support negotiations.
• Monitor global semiconductor trends to mitigate long-lead-time risks. Support Solution Engineering by ensuring "just-in-time" inventory of mission-critical hardware (GPUs, NICs, Switches).
• Partner with Systems Engineering and Architecture teams to translate technical specs into scalable, multi-year procurement roadmaps.
• Oversee the procurement and delivery of integrated components, including NVIDIA Grace CPUs, NVLink, InfiniBand, and ConnectX-8 technologies.
• Architect procurement workflows that satisfy stringent security, data residency, and national compliance requirements for Sovereign AI cloud deployments.

AI Infrastructure Supply Chain Lead

• Develop and implement the AI Market and Solutions strategy to enable high-quality, trusted, and evidence-based AI solutions in education.
• Oversee a portfolio of investments and partnerships that catalyze responsible innovation in AI and EdTech.
• Identify and manage key portfolio risks, developing and implementing mitigation plans.
• Partner with the Strategy, Planning, and Management (SPM) team to develop annual investment plans and budgets.
• Guide the generation and use of evidence to assess AI tools’ efficacy, safety, and fairness.
• Strengthen market incentives and infrastructure for transparent and validated AI adoption across learning and workforce systems.
• Build and lead coalitions with AI companies, EdTech providers, research institutions, and funders to advance responsible AI practices.
• Apply systems thinking to connect data innovations with educational and workforce outcomes.
• Manage partnerships and grantee relationships through site visits, convenings, and evaluation of progress toward milestones.
• Model equitable decision-making and inclusive leadership behaviors, addressing issues promptly and constructively.
• Ensure the team has the resources and conditions needed to thrive and deliver impact.

Deputy Director, AI Infrastructure – Education

• Lead and manage global AI, Storage, and Networking hardware design programs, ensuring on-time delivery, scope control, and budget adherence
• Drive program governance, risk management, and execution excellence across all phases of product development
• Provide regular program updates, risk assessments, and financial reporting to executive leadership through structured reviews (e.g., Leadership Program Reviews)
• Oversee the successful launch of complex hardware platforms, including AI GPU-based systems
• Manage high-priority, resource-constrained programs while maintaining quality and schedule commitments
• Enable innovation in next-generation AI infrastructure and high-performance computing platforms
• Lead end-to-end program management for UALink / PCIe Gen6 switch tray development supporting rack-scale AI platforms and GPU clusters
• Coordinate design, validation, and manufacturing readiness of switch trays across EVT, DVT, and PVT phases
• Drive integration of switch silicon, retimer cards, cabling, and system-level connectivity within full rack-scale architectures
• Collaborate with ODMs and partners to align on design specifications, cost models, and development schedules
• Manage technical trade-offs across performance, signal integrity, power delivery, thermals, and scalability for high-density GPU deployments
• Partner with Tier-1 customers to deliver Joint Design Manufacturing (JDM) programs
• Ensure alignment with customer-specific requirements in design, supply chain, and quality
• Build strong customer relationships and act as a trusted advisor throughout the product lifecycle
• Lead globally distributed teams across engineering (HW/SW), supply chain, manufacturing, and quality organizations
• Coordinate teams across multiple regions (e.g., North America and Asia) to drive seamless execution
• Guide programs through EVT, DVT, and PPVT phases, ensuring technical validation across electrical, thermal, power, signal integrity, and mechanical domains

Principal AI Infrastructure – Hardware Program Management

• Generate new sales leads through outbound phone and email prospecting and in close alignment with the sales team
• Generate new sales leads through inbound efforts, with a focus on proactive follow-up with interested prospects
• Schedule meetings and solution demos for the Account Executives
• Manage outbound leads process to maximise conversion and pipeline development
• Effectively communicate Mirantis’s offerings and apply them to the needs of a prospect
• Accurately enter, update, and maintain daily activity and prospect information in Salesforce
• Achieve monthly quotas and objectives

Sales Development Representative, AI Infrastructure

• Act as the single-threaded owner for operational readiness across NPI and ramp, accountable for outcomes from early bring-up through sustained production
• Translate OpenAI’s infrastructure strategy and engineering objectives into clear operating plans, execution priorities, and decision frameworks
• Drive alignment across Engineering, Operations, Strategic Sourcing, Finance, Capacity Planning, and Executive stakeholders by framing tradeoffs, risks, and recommendations
• Proactively identify inflection points where decisions or investments are required to protect long-term scale, reliability, or cost targets
• Influence operational strategy with manufacturing partners by setting expectations on execution rigor, accountability, and continuous improvement
• Drive overall NPI build readiness, including material accountability, manufacturing and test readiness, product data availability, factory infrastructure, and qualification plans
• Lead transition activities from NPI to mass production, partnering closely with Sustaining Operations teams to ensure seamless ownership transfer
• Translate engineering requirements into actionable, factory-ready plans with tier-1 manufacturing and integration partners
• Lead cross-functional build and debug cadences; ensure issues are clearly owned, aggressively driven, and formally closed with root cause and prevention
• Drive factory and deployment ramp readiness, including throughput, site readiness, yield targets, quality controls, and reliability gates
• Own end-to-end risk management—identification, mitigation planning, escalation, and executive-level communication
• Surface systemic bottlenecks, inefficiencies, or failure modes; propose and implement durable solutions across process, tooling, and organizational interfaces
• Build, formalize, and scale repeatable operating mechanisms that enable predictable, high-velocity hardware development
• Define requirements for operational systems (e.g., PLM enablement), and partner with internal teams to implement, test, and operationalize them
• Establish clear scopes, roles, and responsibilities across internal teams and external partners to ensure clean execution boundaries
• Build strong, trust-based relationships with contract manufacturers, OEMs, and integrators—driving accountability, performance, and transparency
• Support onsite factory builds and field deployments as needed (domestic and international travel)

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