Top AI Automation Jobs in San Francisco Bay Area, CA

Build and deploy AI agent systems that automate complex business processes. Analyze workflows, design and configure agents, integrate AI with enterprise applications, develop connectors and APIs, and establish evaluation and monitoring systems. The role requires expertise in cloud AI services, LLMs, NLP, Python, integration platforms, conversational AI, RAG, vector databases, APIs, and SQL/NoSQL systems. The engineer will also explore emerging AI technologies and mentor junior developers and system administrators.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Build AI-powered software automating hiring, finance, capital planning, contract analysis, and operational workflows. Develop production features using LLM APIs, MCP servers, and agentic frameworks; integrate enterprise systems; transform expert processes into structured data and generated work; and collaborate directly with finance, legal, recruiting, accounting, and delivery teams. The role emphasizes autonomy, rapid execution, product judgment, and forward deployment with business experts.

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Opportunistic role for exceptional candidates to define their own mandate applying frontier AI to biomedical science. Work spans agent platform, science, infrastructure, product, and commercial tasks with end-to-end ownership. Must be highly autonomous, AI-native, collaborative, and able to work onsite in South San Francisco.

Lead and scale a Customer Engineering team supporting production AI workloads. Own operational health, customer escalations, and technical issue resolution. Partner with Product and Engineering to translate customer feedback into product improvements. Drive automation, diagnostics, documentation, and tooling to reduce recurring issues and improve customer outcomes.

Leads enterprise AI and intelligent automation strategy, including Generative AI, machine learning, and RPA initiatives. Manages a multidisciplinary IT team, budgets, vendors, governance, project portfolios, and end-to-end solution delivery. Partners with business and technology leaders to identify automation opportunities, develop business cases, ensure compliance and security, and deliver scalable solutions with measurable business value. The role is hybrid in Camden, New Jersey, with 15% domestic and international travel.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Build and operate an AI-driven global sourcing platform covering web crawling, data ingestion, normalization, entity resolution, product matching, provenance, and source monitoring. Integrate Zoho CRM and QuickBooks, apply LLMs with structured evaluation and human review, and identify automation opportunities across business departments. Lead pilots, train employees, measure impact, and scale the platform to hundreds of sources.

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.

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

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.

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.

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.

Design and execute test strategies and automation for cloud-native and GenAI/AI-ML systems. Perform functional, integration, performance, security, and API testing; validate model accuracy, fairness, robustness, and data quality; integrate automation into CI/CD; track defects; mentor junior QA engineers.

Build scalable QC systems and validation pipelines for RL training data: define quality standards, design metrics and experiments, audit supplier datasets, partner with vendors to diagnose failures, and integrate learnings into infrastructure and vendor tooling.

Build and own an internal AI-powered recruiting pipeline: end-to-end LLM-based sourcing, screening, AI interviews, evaluation logic, and deep integrations with ATS and scheduling to automate hiring workflows.

Design and build automation frameworks and evaluation pipelines for agentic AI products. Develop non-deterministic tests, golden datasets, tool-use validation, prompt regression tests, latency/token monitoring, hallucination detection, and collaborate with AI engineers to convert requirements into measurable automated test cases.

Lead QA Automation for agentic AI products by designing eval pipelines, golden datasets, and automated tests for tool-use, hallucination detection, latency/token monitoring, and regression across models and prompts. Integrate performance testing into CI/CD and collaborate with AI engineers to convert requirements into measurable automated evaluations.