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

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

AI Engineer

• Lead, mentor, and grow a distributed team of AI engineers
• Act as a player-coach: review architecture and contribute to design
• Shape and execute the technical roadmap for the AI Engineering team
• Design and deliver agentic systems end to end
• Build and evolve our internal AI platform
• Partner with leaders from Finance & Supply Chain Systems
• Collaborate closely with peer leaders in Enterprise Architecture
• Champion modern AI engineering practices
• Develop OKRs for the team and report progress
• Recruit world-class AI engineering talent
• Contribute to AI & Business Technology Engineering leadership team planning

Manager, AI Engineering – AI & Business Tech Engineering

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Lead AI Engineer – MLX, Agentic AI, Gen AI platform Services

Lead AI Engineer – Vision Model Customization, VLM

Machine Learning Engineer

• Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers.
• Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI.
• Fine-tune, develop and evaluate machine learning and foundation models.
• Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities.
• Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One.
• Leverage a broad stack of Open Source and SaaS AI technologies.
• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues.
• Retrain, maintain, and monitor models in production.
• Construct optimized data pipelines to feed ML models.
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.

Lead Machine Learning Engineer

Lead Machine Learning Engineer – Manager IC

• Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers.
• Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI.
• Fine-tune, develop and evaluate machine learning and foundation models,
• Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities
• Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One.
• Leverage a broad stack of Open Source and SaaS AI technologies.
• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues.
• Retrain, maintain, and monitor models in production.
• Construct optimized data pipelines to feed ML models.
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.

Senior Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

• Define on-vehicle architecture for producing the core tracking results from the Perception stack. (Senior)
• Assist with the implementation of core tracking results from the Perception stack (Junior)
• Work with both the model and data teams to develop a high-performance, efficient model that can run on a vehicle.
• Work with Perception data, both on the input and output of machine learning models

Senior Machine Learning Engineer – Perception Detection and Tracking

• Lead the end-to-end development and deployment of advanced AI/ML solutions
• Design and implement production-grade machine learning systems
• Drive innovation in emerging AI capabilities
• Develop scalable data science and AI pipelines
• Mentor and guide junior scientists

Applied Machine Learning Scientist II – AI/ML, Fraud/Risk, GenAI, Agentic AI

• Design, implement, and maintain production-grade ML workflows (fine-tuning, batch/online inference, evaluation) with strong observability and CI/CD.
• Deploy GPU-accelerated ML services and jobs using modern tooling and cloud-based orchestration.
• Collaborate with ML scientists and cross-functional teams to capture requirements, scope milestones, and deliver features into user workflows and services.
• Conduct code reviews and mentor peers on software engineering and MLOps best practices.

Software Engineer I/II, Machine Learning

• Support the design, development, and deployment of AI and ML solutions for the Long-Term Care program
• Work with Data Scientists and AI Engineers to help build and maintain ML and GenAI pipelines (data preparation, feature engineering, model training, and evaluation)
• Assist in deploying models into production environments using modern engineering practices
• Help monitor model performance, including accuracy, drift, and reliability, and support troubleshooting and improvements
• Contribute to both traditional ML use cases (predictive models, classification, forecasting) and GenAI use cases (summarization, document processing, RAG, automation)
• Develop and maintain APIs and services that integrate AI solutions into business workflows
• Follow established MLOps and LLMOps practices, including version control, testing, and documentation
• Support building observability tools such as logging, dashboards, and alerts
• Collaborate with cross-functional teams including engineering, data, business, and governance teams
• Ensure solutions follow enterprise standards for security, compliance, and responsible AI
• Continuously learn and apply new tools, frameworks, and best practices in AI, ML, and software engineering

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

• Architect, train, fine-tune, and evaluate state-of-the-art speech representations and ASR models (e.g., End-to-End Conformer, Whisper, RNN-T, and hybrid CTC/Attention architectures) across multiple global languages
• Design and deploy highly scalable algorithms for dynamic vocabulary insertion, contextual biasing, and language model (LM) personalization to precisely capture customer-specific terminology, acronyms, and product names
• Implement automated framework evaluations to benchmark model performance, rigorously tracking Word Error Rate (WER), Character Error Rate (CER), embedding-based metrics, latency budgets (RTF), and computing efficiency profiles under varying acoustic environments
• Develop pioneering multilingual benchmarks for end-to-end conversational AI agents, including speech-to-text and text-to-speech components
• Partner with core engineering teams to build, optimize, and maintain high-throughput pipelines optimized for both ultra-low latency real-time streaming inference and high-efficiency asynchronous (batch) multi-channel speech analysis
• Translate product requirements into technical AI roadmaps, working hand-in-hand with Product Managers to ship speech-to-text, simultaneous translation, and semantic speech analytics features

AI Researcher / ML Engineer – ASR, Speech Specialist

• 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.

Senior AI/ML Engineer

• Annotate documents data from sales-dealers and customers to train AI workflows
• Develop and refine prompts and Retrieval-Augmented Generation (RAG) technique cvg cant es to enhance the performance of CV models and AI workflows
• Assess the needs of dealers and customers, and implement AI verification functionalities to address those needs
• Fine-tune Computer Vision models as time permits and based on the availability of annotated data

Machine Learning Engineer Co-Op, Data Labeller

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

See all jobs on JobTailor

Director of Product Marketing Strategy – AI Infrastructure

AI Research Scientist, Applied AI

Manager, AI Engineering – AI & Business Tech Engineering

Lead AI Engineer – MLX, Agentic AI, Gen AI platform Services

Lead AI Engineer – Vision Model Customization, VLM

Lead Machine Learning Engineer

Lead Machine Learning Engineer – Manager IC

Lead Machine Learning Engineer – Manager IC

Senior Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Senior Machine Learning Engineer – Perception Detection and Tracking

Applied Machine Learning Scientist II – AI/ML, Fraud/Risk, GenAI, Agentic AI

Software Engineer I/II, Machine Learning

AI Engineer

Director, AI Engineering – Agentic Platform

AI Researcher / ML Engineer – ASR, Speech Specialist

Senior Machine Learning Engineer II, Search & Recommendations Ranking

Senior AI/ML Engineer

Machine Learning Engineer Co-Op, Data Labeller

Machine Learning Engineer, Conversion ML