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

Owns application and API services for a multi-tenant, multi-cloud AI platform serving enterprise finance teams. Responsibilities include designing versioned APIs, building review and approval workflows, enforcing authentication, authorization, and tenant isolation, creating auditable systems, supporting customer-hosted deployments, and managing service performance, reliability, observability, and cost. The role also establishes backend patterns, reviews designs, mentors engineers, and partners with data, AI, and product teams.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Lead QA automation strategy and people management while building comprehensive API, UI, backend, performance, security, and database test frameworks. Integrate quality gates into CI/CD across cloud and multi-tenant SaaS environments, validate compliance and data isolation, and communicate quality risks to technical and business stakeholders. Preferred experience includes AI/LLM testing, financial services, observability, chaos engineering, and startup environments.

Build and lead quality engineering for a multi-tenant AI platform. Responsibilities include creating API, UI, backend, performance, security, and database automation; integrating tests into CI/CD; validating cloud and Kubernetes deployments; ensuring tenant isolation and compliance evidence; defining QA standards and release processes; mentoring engineers; and communicating quality risks. Preferred experience includes testing AI/LLM applications, financial systems, observability, chaos engineering, and startup environments.

Build production AI systems for finance teams, including customer-specific anomaly-detection models, grounded natural-language generation, agentic workflows, and managed LLM integrations. Develop Python services with warehouse access and customer-hosted deployments. Implement verification, human-confirmation checkpoints, anti-leakage controls, and CI evaluation for grounding and hallucination. Use customer feedback to improve models and systems while collaborating with AI and evaluation engineers.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Design and build deterministic, auditable AI engines and conversational agents for finance platforms. Lead architecture across RAG, agent orchestration, structured outputs, grounding, evaluation, verification, and human-in-the-loop workflows. Develop production Python services with SQL and vector retrieval, support multi-cloud and on-premises deployments, mentor engineers, conduct design reviews, and communicate architecture decisions to technical and business stakeholders.