Artificial Intelligence Jobs in San Jose, CA

Lead AI FinOps governance to define and track AI cost KPIs, detect anomalies, propose and implement cost optimizations, set spend guardrails with cloud/LLM providers, coordinate cross-functional governance, and present VP-level reporting.

Own and build the agent execution harness and orchestration runtime for enterprise-scale conversational/agentic AI: ensure reliability, observability, prompt/version management, LLM integration, eval frameworks, cost/latency optimization, and provide technical leadership for safe, production-grade agent workflows.

Lead and scale teams building ServiceNow's AI evaluation platform. Define strategy and roadmap for benchmarks, automated scoring, verifier systems, and quality monitoring. Deliver unified internal and customer-facing evaluation services with APIs and dashboards. Drive novel evaluation methods, safety assessment, and cross-functional collaboration to ensure reliable, production-grade AI quality.

Build production-quality developer-facing demos by writing code, integrating models and data, and delivering AI-centric presentations. Create reusable demo assets, partner with product and engineering, and own the AI and data narrative for developer audiences.

Lead a team building evaluation infrastructure and benchmarks for agentic and generative AI. Design automated tests, validate RAG pipelines, benchmark frontier LLMs, and translate evaluation results into product and risk recommendations to ensure reliable, safe, and cost-effective autonomous workflows.

Design, implement, and deploy scalable AI/ML systems. Perform data wrangling, preprocessing, and advanced algorithm development. Collaborate with cross-functional teams to integrate AI solutions, analyze complex datasets for business insights, and support client engagements while continuously improving technical skills.

Support development and implementation of AI governance frameworks, assess governance/risk/controls, gather requirements, document processes, synthesize findings, advise on regulatory compliance, and help design scalable control programs for clients.

Develop and manage AI governance frameworks and risk-control assessments. Translate technical AI concepts for non-technical stakeholders, analyze regulatory and compliance risks, design governance models and controls, interpret data to inform recommendations, build client relationships, collaborate on solutions, and mentor junior team members.

As a Senior Data Scientist, you will lead AI risk assessments, coordinate with stakeholders, and ensure effective AI governance practices for safe financial application deployment.

The Lead AI Engineer will develop and deploy AI components, optimize large language models, and improve AI systems performance while collaborating with cross-functional teams.

Lead the development and deployment of AI-powered products with a focus on foundational AI systems, LLM optimization, and team collaboration to enhance user experiences in banking.

As a Lead AI Engineer at Capital One, you will design and support AI software, develop LLM and optimization techniques, and collaborate with cross-functional teams to innovate AI products.

As a Senior Lead AI Engineer, you will design, develop, and optimize AI systems while collaborating with a cross-functional team to implement AI-driven products.

As a Lead AI Engineer, you will design and develop AI components, collaborate across teams, improve performance, and contribute to Capital One's AI vision.

As a Senior Data Scientist at Capital One, you will lead AI-powered product development using machine learning technologies, focusing on NLP and large language models to enhance customer experiences.

The Senior Lead AI Engineer will design and develop AI-driven products, enhance scalable AI systems, and collaborate with cross-functional teams.

Lead and grow an AI engineering team to design, build, deploy, and support large-scale, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Make strategic build-vs-buy decisions across open-source and SaaS tools, optimize LLM performance for cost/latency/throughput, contribute to technical vision and roadmap, and mentor engineers to advance AI capabilities across the company.

Lead positioning, messaging, and go-to-market for Zero Trust solutions addressing agentic AI. Develop market education, buyer personas, sales enablement, and partner with product to shape roadmap and customer narratives.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Lead and deliver large-scale AI/ML programs and the next-generation Generative AI platform. Build and grow a world-class TPM discipline, manage cross-functional delivery, mitigate technical risk, and drive execution across product, engineering, design, and data science to achieve business impact in regulated environments.

Lead AI engineering teams to develop and deploy AI-powered products. Oversee AI software development, partner with cross-functional teams, and drive innovation in banking AI solutions.

Lead design, development, deployment, and support of foundational AI systems (LLM training/inference, similarity search, guardrails, observability). Optimize large-scale model performance and collaborate with cross-functional teams to deliver responsible, scalable AI products.

Lead design, build, deploy, and support foundational AI systems and products (LLM training/inference, similarity search, guardrails, evaluation, observability). Optimize large-scale AI performance and collaborate with cross-functional teams to produce responsible, scalable AI solutions.

Lead hands-on prototyping and productionization of AI-powered traveler experiences. Define reusable AI patterns, evaluate frontier models, partner with engineering/design/data science, set metrics and experimentation, ensure guardrails and quality, and share playbooks across the company.

Lead strategy and execution for a next-generation AI inference platform: define roadmap, drive inference architecture and orchestration, enable hardware-software co-design, own control plane services and KPIs, and align cross-functional teams to improve latency, throughput, and cost efficiency.