Artificial Intelligence Jobs in Visalia, CA

Leads Zendesk’s global AI Services Consulting organization, establishing strategy, operating models, service portfolios, governance, delivery standards, capacity planning, and commercial performance mechanisms. Builds leadership capability and scalable consulting operations while partnering with Sales, Customer Success, Product, Engineering, Finance, and executive customers. Drives measurable AI adoption, customer value, retention, expansion, delivery quality, and margin through repeatable enterprise services and cross-functional alignment.

Develop and manage AI and ISV partner business development initiatives for Nebius’s AI cloud platform. The role involves collaborating with partners, managing strategic relationships, and supporting growth across AI infrastructure and cloud services. The posting provides limited specific responsibilities or qualifications, but emphasizes English proficiency and authorization to work in the country of application.

Leads HPE’s Enterprise West Storage sales organization across California and Washington. Responsibilities include team development, revenue growth, forecasting, strategic planning, executive customer engagement, partner collaboration, pipeline management, consultative solution selling, and regional financial ownership. The leader drives profitable growth, market share expansion, customer adoption, talent development, and complex enterprise sales pursuits while managing cross-functional stakeholders and channel ecosystems.

Designs, develops, deploys, tests, monitors, troubleshoots, and maintains cloud and on-premise integrations using MuleSoft, Workato, or similar iPaaS platforms. Translates client requirements into integration and transformation solutions, supports production environments, documents implementations, leads SIT and UAT activities, manages change support, resolves integration issues, and mentors developers and architects.

Build and operationalize production AI solutions for federal clients, from stakeholder discovery and architecture through evaluation, deployment, governance, monitoring, and optimization. Develop LLM applications using retrieval, structured outputs, tool use, and agentic workflows. Define quality metrics, create evaluation sets, document limitations, advise clients on AI use cases, and mentor engineers while contributing to reusable internal patterns and products.

Owns AI capabilities end to end, from stakeholder discovery and technical design through development, evaluation, deployment, monitoring, and documentation. Builds LLM applications using retrieval, structured extraction, tool use, and agent patterns; defines measurable quality criteria; balances accuracy, cost, latency, security, and complexity; supports client delivery and proofs of concept; and mentors engineers new to AI.

Build production-quality AI applications for federal clients and internal products. Responsibilities include gathering requirements, designing prompts and retrieval workflows, integrating APIs and data, creating evaluations, investigating failures, documenting limitations, and supporting proofs of concept and client demonstrations. The role requires client-facing communication, comfort with ambiguity, and the ability to translate business needs into working software.

Build Weave’s AI-native lead generation infrastructure from scratch, including agentic workflows, automation, lead sourcing, enrichment, qualification, targeting models, and multichannel campaigns. Partner with account executives and RevOps to define lead quality, maintain HubSpot data hygiene, improve handoffs, and connect lead quality to pipeline conversion. Run data-driven experiments to optimize engagement, conversion rates, and pipeline contribution.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Build and deploy agentic and generative AI copilot and autopilot systems for risk and compliance automation. Own end-to-end ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based model development. Collaborate with product, engineering, platform, and operations teams to improve fraud detection and analyst productivity. Drive strategic roadmaps and cross-functional programs with significant business impact.

Develop and execute growth marketing for enterprise AI systems by translating technical work into compelling stories, videos, demos, and case studies. Shape company positioning, own social and content channels, build marketing tools and agentic workflows, track pipeline impact, and run experiments. The role combines strategic marketing, business storytelling, content creation, coding, and AI research.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Develop end-to-end AI-powered applications, including React/Next.js interfaces, Python or Node.js backend services, APIs, and agentic workflows. Integrate LLM APIs and real-time streaming experiences, design scalable systems, write comprehensive tests, participate in code reviews and on-call support, and contribute to architecture. Collaborate with designers, product managers, and ML engineers to deliver customer-facing products.

The AI Executive Partner will advise C-Suite executives on leveraging AI to drive business outcomes, manage member relationships, and deliver innovative AI solutions.

Lead Strata from inception as co-founder, defining the vision for regulatory intelligence software, engaging enterprise customers, shaping the product roadmap, providing technical leadership for trusted AI agents, and recruiting teams across go-to-market, product, engineering, and customer success. The role requires early-stage B2B SaaS experience, hands-on LLM and agentic architecture expertise, and the ability to build products emphasizing accuracy, citations, auditability, and expert review.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

Supports Relativity Success leadership through strategic planning, prioritization, executive reporting, and business operations. Builds cross-functional relationships, gathers operational intelligence, identifies dependencies and risks, recommends actions, and contributes to annual planning. The role combines analytical rigor, relationship management, process development, and decision support in a SaaS environment.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Provides senior legal counsel on AI, product development, marketing, legal technology, technology transactions, and emerging regulations. Advises Product, Engineering, Marketing, Partner, Customer Success, and Strategy teams; drafts policies and agreement terms; negotiates vendor, open-source, collaboration, and customer agreements; supports product launches and go-to-market strategies; and translates complex AI laws into practical business guidance.

Cover California legal industry and adoption of legal tech and generative AI. Break news, produce enterprise features, cultivate sources across law firms, startups, regulators, and translate technical concepts for a professional legal audience.

Train with engineering, product, and client teams before embedding in client engagements. Discover workflows through SME interviews, document review, and system mapping; identify automation opportunities; write specifications; build and test AI-agent prototypes; lead rollouts; and measure productivity and ROI. This early-career role offers development paths toward AI engineering, product and implementation, or engagement leadership.

Advises legal customers on adopting and integrating Relativity’s AI platform. Builds relationships with lawyers, leads tailored demonstrations, supports pre-sales, onboarding, training, workflow transformation, and value realization. Translates AI capabilities into practical legal use cases, gathers customer feedback for product teams, and develops scalable engagement best practices. The role requires a practicing or former attorney with litigation knowledge, strong communication skills, and hands-on experience with legal AI tools.

Build and operate AI infrastructure systems, including telemetry pipelines, observability dashboards, incident-management automation, hardware and software catalogs, service ownership data models, and self-service discovery tools. Lead cross-functional infrastructure initiatives and develop AI-assisted operational tooling. The role requires production software and infrastructure expertise, programming experience, and the ability to improve reliability, visibility, incident response, and operational efficiency across NVIDIA GPU cloud environments.