LLM Engineer

• Drive the on-site deployment, integration, and scaling of enterprise Generative AI solutions.
• Embed directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across multi-cloud environments (AWS, Azure, GCP).
• Bridge the gap between AI research and production-grade cloud infrastructure.
• Collaborate with cross-functional teams and business partners to leverage analytical skills and ensure business value.

Forward Deployed Engineer, Generative AI

• Design, deploy, and optimize next-generation, customer-facing agentic workflows.
• Work on a unified platform bridging digital discovery and customer operations.
• Use Customer Experience Agent Studio (CX Agent Studio) to build multi-agent applications.
• Implement Code Agents leveraging programmatic SDKs.
• Write custom Python/TypeScript tool callbacks.
• Utilize localized CLI workflows to structure, lint, test, and sync complex agent hierarchies.

Conversational AI Engineer

• Design and build production-grade generative AI systems - agentic workflows, multi-step RAG pipelines, and LLM-powered applications integrated with enterprise data and services
• Define and implement reusable engineering patterns for prompt management, workflow versioning, structured outputs, tool orchestration, and rollback across production AI services
• Apply judgment around model selection and routing, token and latency optimization, cost management, and the appropriate boundaries between AI-driven and deterministic application logic
• Continuously evaluate emerging AI models, tools, and architectural approaches, incorporating improvements into existing systems incrementally
• Integrate AI systems with enterprise data sources, internal APIs, and platforms to enable reliable, production-ready workflows
• Own operational outcomes for production AI systems - reliability, latency, throughput, cost efficiency, and scalability targets
• Implement and maintain monitoring, observability, tracing, and alerting frameworks to ensure operational visibility and rapid issue resolution
• Design and maintain CI/CD pipelines for deployment, versioning, and release management of AI services
• Lead production incident response and root cause analysis, driving systemic improvements that reduce recurrence
• Build and maintain automated evaluation pipelines for LLM outputs - prompt regression testing, retrieval quality validation, and failure mode tracking
• Implement human-in-the-loop controls, content guardrails, schema validation, and structured output enforcement to ensure trusted and auditable AI outputs
• Secure AI systems against prompt injection, data leakage, and unauthorized access, aligning with enterprise compliance and security standards
• Own the team's GenAI technical direction - defining and enforcing engineering standards, patterns, and best practices across all GenAI workstreams
• Make and defend architectural decisions with clarity, providing the technical rationale needed for the Manager and stakeholders to align and move forward confidently
• Work closely with the Manager, GenAI Engineering to receive, refine, and execute on scoped GenAI work - contributing technical judgment to prioritization and tradeoff decisions
• Provide hands-on code review and technical guidance to engineers contributing to GenAI workstreams, raising overall quality through direct feedback and demonstration
• Champion an iterative delivery culture - shipping incrementally, incorporating feedback, and improving continuously in a regular production release cadence

Generative AI Engineer

• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
• Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
• Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
• Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
• Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
• Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
• Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
• Support enterprise AI governance, compliance, auditability, and model risk management requirements.
• Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
• Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
• Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
• Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
• Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
• Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
• Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
• Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
• Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
• Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
• Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
• Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Senior MLOps, Generative AI 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

• 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.

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