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

Lead development and modernization of cloud-native Java and Spring Boot services on AWS. Drive migration from legacy systems, event-driven architecture, automated testing, documentation, production reliability, and secure API design. Build and operate production AI agents using Amazon Bedrock and related frameworks, with evaluation, guardrails, observability, cost controls, and human oversight. Mentor engineers, lead technical direction and code reviews, improve delivery practices, and collaborate across teams on customer data and enterprise applications.

Owns the production ML and GenAI platform, including training and inference pipelines, model serving, registries, monitoring, evaluations, agent gateways, observability, cost controls, and incident response. The role builds reusable deployment patterns, governs agent tools and access, partners with Cloud Engineering, Data Engineering, Security, and SRE, maintains platform architecture, and mentors engineers. Candidates need strong Python and SQL, production ML operations experience, cloud platform expertise, and familiarity with LLM, agent, and MCP architectures.

Own the technical delivery, automation, integration, security, and reliable operation of workforce applications including ADP, Jira, and Confluence. Build workflow automation and AI-powered self-service using scripting, APIs, low-code tools, and governed agents. Partner with business stakeholders to improve productivity, manage enterprise application portfolios, monitor production services, perform root-cause analysis, and establish secure, reusable engineering standards. Mentor peers and support testing, releases, documentation, adoption, and on-call operations.

Designs, builds, and operates AI-powered ServiceNow self-service and workflow capabilities. The role develops reusable actions, secure integrations, conversational experiences, knowledge retrieval, and automated fulfillment while enforcing access controls, testing, governance, and auditability. Responsibilities include production reliability, monitoring, regression automation, root-cause analysis, release management, outcome measurement, technical leadership, code and design reviews, mentoring, and collaboration with product, business, security, workplace, and enterprise AI teams.

Sets enterprise-wide technical strategy as the senior-most individual contributor in Technology. Owns target architecture, agentic AI foundations, cloud migration to AWS and Databricks, data platforms, model serving, experimentation, marketplace systems, performance, resilience, security, and auditability. The role requires hands-on engineering involvement, including code reviews, proofs of concept, design reviews, and incident analysis, while influencing teams and executives without formal authority and developing staff and principal engineers.

Leads the Platform Services organization, managing teams that build internal developer tooling, shared engineering services, golden-path templates, development environments, and AI-assisted development capabilities. Owns platform strategy, service reliability, security, adoption, productivity measurement, and consolidation of duplicated services. Partners with infrastructure and delivery teams, manages budgets and roadmaps, recruits and develops engineering leaders, and drives organizational change and engineering excellence.

Leads the vision, architecture, strategy, and execution of enterprise data platforms supporting scalable analytics and AI initiatives. Oversees modern data technologies, production reliability, FinOps, governance, architecture standards, and cross-functional delivery. Builds and mentors engineering teams, communicates with executives, evaluates third-party technologies, resolves critical production issues, and drives continuous improvement. The role is remote with occasional planned travel to the Southfield, Michigan office.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Owns the technical strategy, roadmap, requirements, and delivery of scalable data solutions. Partners with business, product, design, and engineering teams to prioritize high-impact features, assess vendors, guide architecture, incorporate AI strategies, and resolve technical issues. The role requires strong BI and data warehouse expertise, technical leadership, stakeholder communication, agile product development, customer delivery support, and mentoring responsibilities.

The Senior Data Modeler designs and maintains data models, supports AI-driven practices, and collaborates on data lifecycle processes. Responsibilities include translating business needs into data structures, ensuring data governance, and supporting analytics and AI use cases.