Top AI Automation Jobs in San Francisco Bay Area, CA

Leads enterprise AI transformation by developing the AI roadmap, prioritizing investments, managing initiative portfolios, and delivering measurable business value. Partners with executives and cross-functional teams to redesign operations, establish governance, evaluate technologies and vendors, drive adoption, and lead workforce change. Accountabilities include revenue growth, productivity, efficiency, customer experience, compliance, AI maturity, and value realization.

Founding developer relations role owning developer discovery, docs, tutorials, runnable examples, community (Discord), events, and AI-powered content automation. Write deep technical posts, keep docs current, run community engagement, measure content impact, and feed product learnings back to engineering. Hands-on builder who reads code, ships examples, and scales DevRel systems.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Design and author practical courses on AI and workflow automation adoption. Develop curricula, case studies, templates, exercises, and frameworks; translate implementation experience into actionable learning content; review technical accuracy; facilitate sessions; and teach responsible AI controls, workflow redesign, scaling, and value measurement.

Build and deploy AI-enabled automation solutions across enterprise systems. Partner with Business Applications, Data & Analytics, and IT teams to analyze processes, identify automation opportunities, integrate platforms, test and monitor solutions, document implementations, and train process owners. The role requires hands-on coding, AI workflow experience, enterprise integrations, scripting, SQL, structured data knowledge, production support, and strong communication with technical and non-technical stakeholders.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Develop and manage intelligent automation solutions using UiPath, bridging communication between project managers and clients, conducting process analysis, and providing documentation.

Build and deploy AI applications, agents, intelligent workflows, document-processing solutions, predictive analytics, and automation. The role spans AI use-case assessment, solution design, prototyping, evaluation, testing, production deployment, monitoring, governance, and responsible-AI controls. The engineer will integrate AI with enterprise systems, APIs, workflows, and data platforms while partnering with engineering, cybersecurity, governance, and mission teams in a federal environment.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Build and deploy AI-powered automations, internal tools, scripts, dashboards, and reporting systems across operations, growth, finance, fulfillment, supply chain, and customer experience. Work directly with the CEO and functional leaders to identify manual bottlenecks, develop end-to-end solutions, support forecasting and unit economics, and execute high-priority projects. The role requires strong ownership, analytical ability, technical fluency, and comfort operating across business functions in an intense onsite environment.

Support AI project teams by executing workstreams, preparing client-facing presentations and documentation, assisting implementation activities, maintaining required certifications, using relevant technology, communicating with stakeholders, and traveling as needed for client engagements.

Lead Net at Work’s transformation into an AI-native company by redesigning workflows, deploying virtual workers, building AI capabilities, training employees, and managing adoption. Establish the internal AI tool stack, standards, and use-case pipeline while delivering measurable operating leverage through reduced cycle times, improved quality, increased capacity, and margin gains. Validate results with Finance and drive organization-wide change through influence, process redesign, and strong stakeholder leadership.

Leads and grows a client-facing AI and data services practice, owning strategy, offerings, delivery methods, team development, client outcomes, sales support, and P&L performance. Guides clients from data readiness and AI use-case discovery through production deployment, adoption, and measurable business results. Advises executives, scopes complex engagements, builds delivery teams, and develops managed, subscription, and advisory services.

Design, implement, and optimize AI-powered workflow automation solutions. Analyze business processes, document current and future states, gather requirements, integrate automation tools, support testing and deployment, monitor performance, and identify improvements. Collaborate with business and technical stakeholders, provide training and troubleshooting support, promote automation best practices, and contribute to digital transformation and innovation initiatives.

Enable and mentor partners to sell and deliver NiCE AI/CCaaS solutions. Lead technical discovery, demos, PoVs, and architecture reviews; build partner enablement content; manage technical partner relationships, integrations, release impacts, and service scoping.

The Principal Consultant leads AI and digital transformation engagements, guides clients in identifying AI opportunities, builds strategies and roadmaps, implements solutions, and develops teams to achieve measurable business outcomes.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Build and ship AI-native systems for a fintech app: AI agents, RAG/retrieval systems, tool-calling and automation for onboarding, KYC, support, risk, CRM and operations. Work with product and engineering to design reliable, low-hallucination, cost- and latency-aware solutions and turn manual processes into scalable AI-enabled systems.

The SME will develop a curriculum on AI for finance, creating engaging content and videos to train finance professionals in AI applications. They must leverage their extensive finance experience and AI understanding to enhance learner success.

Designs and deploys AI assistants, copilots, custom agents, and end-to-end workflow automations across business functions. Builds integrations between AI platforms and enterprise systems using APIs, middleware, and serverless architectures. Develops prompt, RAG, and agentic solutions while measuring ROI and adoption. Partners with IT Operations and Security on governance, privacy, compliance, and secure deployment. Documents solutions, trains teams, and continuously improves automations based on usage and feedback.

Design and build AI- and automation-driven solutions to accelerate Mortgage Cadence Platform configuration using LLMs and workflow automation. Develop integrations with system APIs, partner with implementation teams, create documentation and training, and propose new tools to improve implementation accuracy and scale.

Design and productionize ML/DL algorithms and agentic LLM triage systems, run and maintain multiple production data pipelines (Databricks/AWS), monitor and fix pipeline issues, build monitoring tools and dashboards, and collaborate with data science, planning, safety, and QA stakeholders.

Build and productionize AI-powered internal automation tools, including RAG workflows, intelligent agents, integrations, and reusable automation components. Partner with Sales, Marketing, and Finance AI Champions to identify workflow bottlenecks and enable safe self-service. Improve TDD, CI/CD, infrastructure-as-code, monitoring, logging, and observability practices while maintaining reliable internal APIs and workflows. Establish a scalable component library and operational model for enterprise automation.

Design, build, and deploy production agentic AI systems for internal operations and client-facing services. Own AI architecture, agent orchestration, integrations, deployment pipelines, governance, monitoring, and continuous improvement. Develop productized AI offerings, evaluate emerging platforms and LLMs, define KPIs, and connect deployments to measurable business outcomes. Collaborate with executive and engineering teams while establishing the foundation for a scalable AI practice.

Lead enterprise adoption of Microsoft Copilot, Copilot Studio, and Power Platform by analyzing business processes, designing AI agents, building ROI-backed automation roadmaps, delivering training and prompt engineering best practices, and measuring utilization and productivity improvements across functions.