Artificial Intelligence Jobs in San Jose, CA

Lead design, build, and deploy production-scale generative AI and agentic systems: foundation model training, LLM inference, vector search, guardrails, evaluation, observability, and performance optimization across cloud infrastructure.

Designs and delivers AI-augmented enterprise integration platforms, API ecosystems, and autonomous workflows. Responsibilities include developing event-driven distributed systems, integrating SAP and SaaS applications, managing API lifecycles, supporting EDI deployments, implementing secure identity and access controls, and building CI/CD and observability capabilities. The role uses agentic AI, workflow automation, and intelligent monitoring to enable self-healing, adaptive integration across cloud and enterprise environments.

Conduct applied AI research, designing, training, fine-tuning, evaluating, and productionizing language models. Develop reinforcement learning, agent simulation, and reusable training methods for enterprise applications across networking, security, observability, and IT operations. Collaborate with researchers and engineers to establish model quality gates and translate research into scalable customer and internal solutions.

Develops strategic partnerships and enterprise opportunities for Intel-based Physical AI and robotics solutions. Builds adoption strategies, business cases, go-to-market plans, account plans, and co-sell motions with cloud providers, systems integrators, software partners, robot OEMs, and AI model providers. Positions Intel technologies, represents market needs internally, influences product roadmaps, and drives ecosystem-led deployment scale across industries.

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.

Manage the full eDiscovery lifecycle for litigation matters: scope and execute ESI collection, processing, review, and production. Use Relativity One, BrainSpace, iPro and other tools, ensure QC and metrics reporting, coordinate cross-functional stakeholders, mentor teams, and produce attorney-facing reports.

Leads engineering for an AI-native security operations product suite. Responsibilities include guiding architecture, setting engineering and product strategy, developing talent, engaging customers, and applying emerging AI innovations. The role requires leading high-velocity teams, shaping product roadmaps, coding with AI agents, building cloud software across AWS, GCP, and Azure, and collaborating with globally distributed teams.

Leads the architecture, strategy, and implementation of applied and agentic AI capabilities for HPE private and hybrid cloud offerings. Designs production-grade AI architectures, multi-agent systems, LLMOps, AI security, governance, observability, and knowledge layers. Builds reusable AI components, optimizes large-scale AI systems, guides technology selection, and converts proofs of concept into reliable services. Influences customers and internal teams through technical workshops, reference architectures, publications, and presentations while mentoring engineers and architects.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.

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.

Support the ISG executive leader by driving strategy, operations, and cross-functional program execution. Lead strategic planning, KPI operating model, business reviews, communications, and coordination with corporate support teams to enable division objectives and strategic initiatives.

Lead the development of AI software components for Capital One, including model training, inference, and system optimization. Collaborate with cross-functional teams to deliver groundbreaking AI solutions, ensuring quality and scalability while mentoring junior team members.

The role involves developing AI foundation models, collaborating with cross-functional teams, and conducting applied research to enhance customer experiences using advanced AI techniques.

As a Lead AI Engineer at Capital One, you will design and develop AI systems, collaborate with cross-functional teams, and optimize AI solutions for performance and scalability.

Lead design, development, deployment, and optimization of large-scale AI/ML systems (training, inference, orchestration, observability). Collaborate cross-functionally to build responsible, scalable generative AI and agentic platforms, improve performance and cost, and shape long-term ML systems roadmap.

The role involves partnering with cross-functional teams to design, develop, and deploy AI software solutions, focusing on large language models and AI optimization techniques.

Develop and productionize AI agents and features for Cisco’s Circuit platform. Conduct applied research on model routing, batch capacity, fine-tuning, token optimization, and real-time workload management. Evaluate and integrate new AI models, improve cost and performance, and apply agentic AI to business automation. Lead feature delivery from concept through production, assess AI-generated code quality and security, drive cross-functional alignment, and mentor engineers.

Designs and deploys end-to-end Physical AI and robotics solutions across data collection, simulation, model training, optimization, robotic hardware deployment, and fleet operations. Advises enterprise customers, cloud providers, systems integrators, and ecosystem partners on integrating AI workloads with IT, OT, edge, and cloud environments. Leads proofs of concept, technical evaluations, benchmarking, and solution validation while influencing Intel product roadmaps, strategy, and customer adoption.

Manage business operations for a developer ecosystem team by maintaining activation calendars, budgets, priorities, trackers, and roadmaps. Build AI agents, dashboards, scripts, and workflow automations to improve reporting and reduce manual coordination. Partner with team leads to identify risks, influence meetings and strategy reviews, establish scalable processes, communicate changes, and drive adoption of improved operating rhythms and tools.

Leads AI and legal technology enablement across West Coast law firm offices. Designs training curricula, academies, workshops, communications, and adoption campaigns; manages change initiatives; builds attorney stakeholder relationships; analyzes usage data; reports progress and risks; and promotes responsible, habitual technology adoption among attorneys and practice groups.

Build and deploy production-grade AI applications using Python and Azure. Develop LLM, RAG, and agentic workflows; integrate enterprise platforms, APIs, databases, and Microsoft 365; and create scalable backend services. Partner with stakeholders to translate ambiguous business needs into production solutions while applying strong testing, security, observability, scalability, and performance practices.

Lead regional enterprise sales for NVIDIA’s Physical AI and digital twin solutions. Build executive customer and partner relationships, develop end-to-end solution sales plays, enable global sales teams, manage lighthouse opportunities, track market adoption, and provide customer insights to product teams. The role focuses on industrial manufacturing, factory planning, robotics, synthetic data, intelligent infrastructure, and expansion across APAC and the Americas.

The AI Deployment Engineer will build, deploy, and scale AI solutions while collaborating across departments to ensure successful implementation and support of operational workflows.

Design and build AI-powered cybersecurity platforms that ingest and normalize massive security telemetry, correlate events, predict threats, and generate explainable findings. Develop Python backend services, distributed storage systems, and AIDevOps pipelines for rapid experimentation. Integrate foundation models, agentic systems, RAG, embeddings, and graph-based models into analyst workflows. Collaborate with product teams, security experts, and engineers to deliver scalable, trusted threat detection and automation capabilities.

Design and build AI-native cybersecurity platforms that analyze large-scale security telemetry, correlate events, predict threats, and generate explainable alerts. Develop scalable distributed systems, data pipelines, AI DevOps workflows, agentic systems, RAG and embedding solutions, and model integrations. Collaborate with security researchers, SMEs, product managers, and engineering teams to create trusted automation and analyst-focused threat detection, investigation, and response capabilities.