Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design and deploy enterprise AI agents and agentic workflows using cloud-native architectures. Integrate multiple AI providers, build scalable platforms, establish evaluation and observability practices, and tailor solutions to client domains. Lead client workshops, prototypes, code-with sessions, and technical advisory engagements while developing reusable engineering patterns and best practices.

Provides onsite logistics and technical support for smart devices. Responsibilities include receiving, tracking, distributing, returning, configuring, troubleshooting, and shipping devices; maintaining inventory and documentation; supporting users; and developing diagnostic scripts, automation, and web applications. The role is fully onsite in San Jose and requires flexibility between logistics and technical assignments.

Designs, builds, and scales enterprise AI applications using classical machine learning, generative AI, RAG, and agentic architectures. Develops intelligent workflows, autonomous agents, knowledge systems, chatbots, copilots, and decision-support solutions integrated with enterprise platforms and APIs. Leads AI governance, evaluation, observability, security, and human-in-the-loop controls while advising clients on enterprise AI transformation. Requires extensive experience with Claude, OpenAI, LLMs, cloud platforms, vector databases, semantic search, and production-grade AI implementations.

Designs enterprise data fabric solutions across on-premises and multi-cloud environments. Integrates data virtualization, metadata, knowledge graphs, cataloging, and automated integration into connected data layers supporting analytics and AI. Defines scalable reference architectures, governance patterns, data products, self-service consumption, and real-time delivery. Advises client architects and leaders on data modernization initiatives using cloud platforms, data mesh, APIs, and streaming architectures.

Leads enterprise data engineering, data fabric architecture, governance, and AI platform delivery. Designs cloud data platforms, ETL/ELT pipelines, ontologies, APIs, streaming architectures, metadata lineage, and data quality controls. Integrates Salesforce, Palantir Foundry, and AI/ML workflows while enabling KPI measurement and business value realization. Advises clients, defines reference architectures, maintains platform integrity, and drives reusable data assets and modernization solutions.

Develop and deploy machine learning models and responsible AI solutions for clients. Evaluate and mitigate fairness, bias, transparency, explainability, robustness, privacy, and other AI risks. Manage the full AI development lifecycle, optimize existing systems, document methodologies, contribute to responsible AI standards and training, and support client proposals. The role requires collaboration, technical communication, and potentially extensive travel.

Lead client workforce transformation initiatives focused on AI-enabled work redesign, skills-based talent strategies, workforce planning, talent intelligence, reskilling, organizational transformation, and employee experience. Use generative AI, LLMs, workforce analytics, labor market data, and talent platforms to develop strategic recommendations and future-ready solutions. Support change initiatives, proposals, thought leadership, client relationships, and internal capability-building efforts, with travel as needed.

Develop and execute enterprise AI and data strategies for clients, including AI value cases, operating models, talent strategies, technology roadmaps, responsible AI plans, and commercialization opportunities. Build trusted C-suite relationships, lead transformation workstreams, shape new business opportunities, and guide large-scale AI-enabled reinvention initiatives. The role requires consulting experience, strong program management and communication skills, leadership capability, and up to 80% travel.

Design, build, deploy, and operate production AI-native systems spanning applications, APIs, data, cloud infrastructure, DevSecOps, security, observability, and agentic AI. Lead architecture, client engagements, multidisciplinary engineering teams, and technical delivery from ambiguous requirements through production. Build cloud-native platforms, CI/CD pipelines, secure data and context architectures, AI integrations, agent orchestration, evaluations, and production safeguards. Establish reusable engineering standards, tooling, and practices for enterprise AI adoption.

Designs, implements, and supports secure, scalable enterprise network solutions. Responsibilities include assessing client environments, developing network designs and transformation roadmaps, implementing cloud and data center infrastructure, evaluating emerging technologies, supporting automation, documenting findings, presenting recommendations, and managing client relationships. The role requires experience with network modernization, hybrid and multi-cloud architectures, software-defined networking, DevOps operating models, high-performance networks, and distributed systems. Travel may range from 25% to 100%.