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

• 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

• 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

• 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

• 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

• 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)

Supply Chain Operations Program Manager – AI Infrastructure

• Act as a Strategic AI Client Director within the assigned West Coast territory, driving adoption of DDN’s AI infrastructure solutions across enterprise and research organizations.
• Drive revenue growth and achieve or exceed assigned sales quota across the West Coast region.
• Develop and execute strategic territory and account plans targeting organizations building large-scale AI and high-performance computing environments.
• Identify and qualify new opportunities across industries including financial services, healthcare, telecommunications, technology, research institutions, and large enterprise organizations.
• Build trusted relationships with senior technical leaders, AI infrastructure teams, and executive decision-makers within target accounts.
• Partner with solutions architects and engineering teams to design and present AI infrastructure and high-performance data storage solutions.
• Deliver compelling presentations, product demonstrations, and proposals showcasing the value of DDN’s AI-optimized storage platforms.
• Manage the full enterprise sales cycle including pipeline development, forecasting, contract negotiation, and deal closure.
• Develop strong relationships with channel partners, system integrators, OEM partners, and distributors to expand market reach.
• Stay current on market trends related to AI infrastructure, GPU computing, large-scale model training, and high-performance data environments.
• Represent DDN at industry events, conferences, and customer engagements to generate new opportunities and strengthen market presence.

Client Director – Strategic AI Infrastructure

• Lead Technical Strategy for Joint Offerings: Act as the primary technical lead for defining the vision of joint offerings.
• Direct engagement with NVIDIA, AMD, Intel and OEM (HPE, Dell, Lenovo) product leadership to align future roadmaps and identify strategic opportunities.
• Engage key GTM leaders to align those offerings into VAR, GSI, and ISV based offerings.
• Requirement Sourcing & Prioritization: Serve as a primary conduit to the Ecosystem Engineering team.
• You will source and prioritize the technical requirements for what needs to be architected, prototyped, or co-created, providing the strategic "why" and "what" so Engineering can execute the "how."
• Perform initial architectural scoping specifically to identify the technical gaps and requirements needed for new offerings, ensuring a smooth hand-off to engineering for detailed design.
• Lead contribution of key partner product, solution, and use-case strategies for AI Infrastructure partners in the overall AI Ecosystem strategy.
• Collaborate with the AI Tech Sales team and the AI Tech Sales Eco Global Leader to ensure that technical offering definitions are successfully transitioned into field-ready sales motions by establishing early input and ongoing feedback mechanisms.

Senior AI Infrastructure Tech Strategist

• Lead planning and execution of major AI infrastructure initiatives spanning training pipelines, data systems, model evaluation, and inference/serving
• Build structures that keep teams aligned: scopes, goals, requirements, timelines, risks, and success metrics
• Partner with engineering, research, and product to translate model and product needs into infrastructure roadmaps and priorities
• Drive cross-functional accountability and communication across teams working on tightly coupled systems
• Track key infrastructure metrics (e.g., reliability, latency, throughput, cost efficiency) and define reporting that surfaces progress and risk
• Identify bottlenecks in infrastructure workflows and lead efforts to improve tooling, automation, and developer velocity
• Support capacity planning and resource allocation to ensure infrastructure scales with model and product growth
• Develop repeatable frameworks and operational patterns that improve execution quality across infrastructure programs
• Serve as a strategic partner to leaders on prioritization, sequencing, and tradeoffs across infrastructure investments.
• Work closely with AI cloud providers and serve as the primary bridge between internal engineering teams and external partnership efforts.
• Technical understanding of GPU clusters, AI accelerator chips, and cloud providers to effectively evaluate requirements and guide infrastructure decisions.

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