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

Machine Learning Engineer

• Design, build, and operate end‑to‑end AI/ML solutions (data pipelines, models, services, and tools) for diagnostics, prognostics, and test analytics.
• Implement production‑grade ML pipelines on platforms such as Azure and Databricks, covering data ingestion, feature engineering, training, evaluation, and inference for batch and streaming workloads.
• Develop and maintain robust, observable ML services and internal tools that make complex vehicle and field data easy to use for engineers and technical stakeholders.
• Apply practical ML and statistical methods (e.g., tree‑based models, time‑series and anomaly detection, deep learning where appropriate) with a focus on reliability, explainability, and impact.
• Own model and data observability in production, including metrics, dashboards, alerts, and remediation workflows for drift, data quality, and performance regressions.
• Partner with data engineering to define and use industrialized and vectorized data products that support search, RAG, and analytics at scale.
• Review designs and code, mentor AI/ML practitioners, and help set high standards for testing, logging, deployment, and documentation.
• Collaborate with diagnostics/prognostics SMEs, validation, safety, and program teams to prioritize work, define success metrics, and embed solutions in day‑to‑day engineering workflows.

Staff Artificial Intelligence Machine Learning Engineer

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• 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

AI Engineer

• Design, develop, and optimize advanced data pipelines using technologies such as Apache Spark, Kafka, Airflow, and cloud-native tools like AWS Glue, Azure Data Factory, or Google Dataflow to drive business insights and enable automation.
• Collaborate with data scientists, architects, and business stakeholders to transform raw data into actionable intelligence, while architecting and maintaining robust data solutions across data lakes, warehouses, and marts using platforms like Snowflake, Redshift, or BigQuery.
• Develop, deploy, and monitor AI/ML models with frameworks such as TensorFlow, PyTorch, and Scikit-Learn, and operationalize these models via APIs and batch or streaming services.
• Ensure data quality, security, and compliance by applying best practices in data governance, encryption, and access control, leveraging tools such as Apache Atlas, Collibra, or cloud security features.
• Research and prototype cutting-edge AI/ML algorithms, evaluate emerging technologies, and communicate findings through technical documentation, dashboards (e.g., Power BI, Tableau), and presentations.
• Champion best practices in CI/CD, automation, and code review to continuously improve the performance, scalability, and reliability of the organization’s data and AI ecosystem.

Lead Data/AI Engineer

• Own AI architecture across cloud platforms (AWS Bedrock, Google Vertex AI)—you'll make the calls on what we build and how
• Design RAG systems end-to-end, from document ingestion and chunking strategy through retrieval optimization and generation quality
• Build agentic systems using modern patterns: MCP (Model Context Protocol), tool-use, function calling, and orchestration frameworks like LangGraph
• Define observability and evaluation so we know our AI is actually working in production—not just demo-ing well
• Manage cost at scale—model selection tradeoffs, token economics, and budget forecasting as we grow
• Establish guardrails and compliance patterns, particularly for healthcare data and HIPAA requirements
• Mentor and guide implementation-level developers as the team grows
• Ship production systems—this isn't a strategy-only role; you'll write code and deploy real solutions

Senior AI Developer – Architect

• Explore and evaluate emerging AI tools, platforms, and agent-based solutions to identify opportunities for the business.
• Design, build, and integrate AI-powered workflows, services, and applications into enterprise systems.
• Help shape and develop internal AI platforms, including agent architectures, permissions, and governance guardrails.
• Guide AI initiatives through structured evaluation and approval processes, ensuring alignment with technical and governance standards.
• Collaborate with teams across Architecture, Security, DevOps, Legal, Privacy, and the wider business to deliver well-rounded AI solutions.
• Translate complex or ambiguous business needs into clear technical requirements, success metrics, and deliverables.
• Monitor performance, adoption, and usage of AI solutions, identifying trends, risks, and opportunities for optimisation.
• Maintain clear documentation, including AI use cases, tool registries, and lifecycle records, ensuring audit-ready standards.
• Support the development of a centralised AI knowledge base to drive consistency and accelerate adoption across teams.
• Contribute to continuous improvement of AI governance, development practices, and delivery frameworks.

AI Engineer, AI Analyst

• Manage one or more Applications Development teams in an effort to accomplish established goals as well as conduct personnel duties for team (e.g. performance evaluations, hiring and disciplinary actions)
• Utilize in-depth knowledge and skills across multiple Applications Development areas to provide technical oversight across systems and applications
• Review and analyze proposed technical solutions for projects
• Lead the design, development, and implementation of sophisticated AI/ML, Generative AI, and Agentic AI solutions, including AI assistant tools, in close collaboration with AI architects, product owners, and cross-functional engineering teams within the Engineering Excellence framework
• Architect and develop end-to-end AI systems, leveraging both Python for machine learning workflows and Java for scalable enterprise-grade backend services, ensuring seamless integration with existing and new applications
• Design and implement intelligent agentic systems that exhibit autonomous decision-making capabilities, enabling advanced automation and self-optimizing processes
• Develop, fine-tune, and optimize Large Language Models (LLMs) using both parameter-efficient techniques and full fine-tuning, focusing on their integration into robust, production-ready systems supported by both Python and Java components
• Drive the implementation and experimentation with advanced generative AI methods such as prompt engineering and Retrieval-Augmented Generation (RAG), ensuring their effective deployment and performance in enterprise environments
• Own the deployment pipeline for AI models and associated services, leveraging CI/CD methodologies, containerization (Docker), and orchestration (Kubernetes) on leading cloud platforms (AWS, Azure, GCP) to ensure secure, scalable, and automated releases
• Apply deep knowledge of database technologies (SQL and NoSQL) to design efficient data storage, retrieval, and management strategies for AI applications, ensuring data integrity and performance
• Contribute to the establishment and enforcement of engineering best practices, architectural patterns, and tooling standards for full-stack AI development, advocating for maintainability, observability, and cost-efficiency
• Stay abreast of the latest advancements in AI/ML, Agentic AI, cloud technologies, and DevOps trends, proactively sharing knowledge and driving adoption of innovative solutions
• Ensure strict adherence to ethical AI guidelines, data privacy regulations, and compliance standards throughout the entire AI solution lifecycle
• Act as a mentor for junior engineers, providing expert guidance on Python, Java, cloud technologies, and CI/CD best practices, fostering a culture of technical excellence and continuous improvement.

Senior AI/ML Engineer – Full Stack Developer

• Design AI solutions for customer experience use cases, including self-service, agent assistance, journey management, and back-office automation.
• Support solution designs that combine LLMs, deterministic workflows, tools, orchestration layers, fallback strategies, and human handoff patterns.
• Support retrieval-augmented generation, knowledge, and content strategies that help ground AI responses in trusted enterprise data.
• Assist with approaches for content structure, chunking, retrieval, relevance, and freshness while balancing performance, latency, and governance considerations.
• Help define AI evaluation approaches and quality measurement strategies for customer-facing AI experiences.
• Support the creation of test sets, feedback loops, and success metrics tied to accuracy, containment, customer satisfaction, business impact, and adoption.
• Assist in designing contextual AI experiences that use real-time data, conversation state, customer information, interaction history, and external signals to create more relevant and personalized customer and employee experiences.
• Support solution design decisions related to scalability, latency, reliability, and cost efficiency in enterprise environments.
• Help evaluate trade-offs across model selection, caching, orchestration, architecture patterns, and production-readiness considerations.
• Leverage strong working knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support presales activities including technical discovery, solution design, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, success criteria, and differentiated Genesys AI value.
• Partner with account teams and Professional Services to help transition successful prototypes into production pilots, including technical handoff, solution hardening, KPI alignment, and business outcome validation.
• Build and support integrations to third-party systems via RESTful APIs and emerging interoperability patterns, showcasing what is possible with Genesys Cloud AI solutions.
• Develop reusable technical assets and help enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide field feedback and customer insights to Product Management and Engineering related to AI product design, implementation considerations, and customer-driven enhancements.
• Engage with technical and business stakeholders to position Genesys AI as part of a customer’s broader customer experience, IT, and transformation strategy.

AI Architect

• Responsible for architecting solutions achieving customer business outcomes
• Develop and articulate compelling, accurate, and relevant proposals
• Provide technical expertise to sales teams and customers
• Develop customer proposals and critically review them
• Quantify the impact of business problems and position business value
• Communicate how the solution value propositions address customer business needs
• Drive the Account Business Planning process leveraging knowledge of industry trends and customer’s technical environment

AI Architect – Pre-Sales

• Support the deployment, monitoring, and ongoing maintenance of media measurement and customer modeling systems in partnership with Data Science and Engineering teams.
• Develop and maintain SageMaker processing and training jobs, model endpoints, and supporting infrastructure across development, testing, and production environments.
• Contribute to Step Functions, Lambda functions, and Airflow (MWAA) workflows that orchestrate model training, scoring, retraining, and analytics pipelines.
• Support MLflow model registration and promotion processes, configuration management, and versioned model artifacts.
• Build and maintain Docker images, ECR repositories, and GitLab CI/CD pipelines to enable reliable model deployment and release processes.
• Help productionize machine learning models and data pipelines that support customer analytics, scoring, and decisioning use cases.
• Investigate and resolve production issues using CloudWatch, DataDog, SageMaker logs, and workflow monitoring tools.
• Collaborate with cross-functional partners to implement platform enhancements, improve operational reliability, and deliver new capabilities.
• Contribute to engineering best practices, documentation, testing strategies, and operational procedures.

Senior Machine Learning Engineer

• Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale.
• Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments.
• Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack.
• Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery.
• Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams.
• Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem.
• Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team.

Staff Machine Learning Engineer – ML Training Infrastructure

• Define and execute the roadmap for runtime intelligence capabilities
• Partner with executive stakeholders to align runtime intelligence investments with enterprise priorities
• Lead development of core runtime capabilities: Context orchestration and state management, Memory systems, Retrieval systems, Personalization frameworks
• Establish observability standards across runtime intelligence systems
• Oversee end-to-end delivery of runtime intelligence platforms

Director, Machine Learning Engineering

• Establishes the architectural patterns, evaluation practices, and deployment standards for the team
• Makes framework and model recommendations that set the foundation for how the team builds — evaluates orchestration frameworks, selects deployment patterns, trains and fine-tunes models, and determines where managed platforms end and custom build begins
• Translates proposed business solutions into technical plans — defines product life cycles, prioritizes the backlog, and breaks initiatives into buildable work
• Owns solutions end-to-end: technical planning, architecture, build, deploy, and the monitoring that keeps them honest in production
• Builds the monitoring, evaluation, and regression detection systems that keep production agents reliable — including logging, performance benchmarking, and feedback loops that surface drift early
• Partners with data governance to ensure solutions meet compliance, data quality, and operational standards

Lead AI Engineer, Business Operations

• 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

• Evaluate emerging AI tools, platforms, and agents
• Conduct structured technical assessments
• Build and integrate AI workflows and services into enterprise systems
• Collaborate with Architecture, Security, DevOps, Legal, Privacy, and business stakeholders
• Monitor adoption metrics, cost and consumption trends, quality indicators, and post-release performance

AI Engineer / AI Analyst

• Collaborate closely with customers to scope, deploy, and maintain AI solutions in production environments.
• Debug and optimize data pipelines and AI systems running on customer networks.
• Translate complex, often ambiguous customer requirements into well-scoped technical solutions.
• Work across the stack—from model inference on consumer hardware to infrastructure automation.
• Read hardware schematics/logs to identify performance bottlenecks and suggest improvements.
• Serve as a trusted technical advisor to enterprise clients, representing the engineering team externally.
• Contribute feedback and insight to internal teams to continuously improve product robustness and usability.

Forward Deployed AI Engineer

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

• Participate in end-to-end AI/ML projects, from research and design through deployment and ongoing maintenance, to deliver scalable production systems.
• Implement ML pipelines and production-grade infrastructure that support high-volume workloads and reliable model execution.
• Partner with cross-functional teams to integrate AI capabilities that align with business goals and customer needs.
• Research and prototype new AI/ML algorithms and techniques to support innovation and product development.
• Help establish best practices for AI/ML development, deployment, monitoring, and governance across the team.
• Monitor model performance, scalability, and cost in production and contribute to continuous optimization.
• Apply AI tools and techniques in day-to-day engineering work to improve productivity, support experimentation, and strengthen evaluation of model behavior, outputs, and system impact.
• Advocate for responsible AI by considering fairness, transparency, data privacy, and appropriate use in model design and deployment.
• Collaborate with engineering partners to move models from prototype to production using repeatable, maintainable approaches.
• Document technical decisions, model behavior, and operational considerations to support shared understanding and long-term maintainability.

See all jobs on JobTailor