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

Lead Quantiphi’s co-sell motion with frontier AI partners including OpenAI, Anthropic, and LangChain. Build partner-seller relationships, originate and qualify enterprise AI opportunities, coordinate multidisciplinary pursuit teams, manage account and pipeline reviews, and drive proposals and commercial closures. Translate customer AI adoption needs into implementation services engagements, support production deployments, and develop repeatable sales plays and expansion opportunities.

Architect and build cloud-native data platforms, Snowflake pipelines, and datalakes for complex financial datasets. Lead legacy modernization, ETL/ELT, reconciliation, data quality, governance, and regulatory reporting initiatives. Drive technical product roadmaps, backlog prioritization, requirements, testing, cutovers, and releases. Coordinate Agile delivery through Jira and Confluence while aligning business, analytics, and engineering stakeholders across the full data and software lifecycle.

Lead DevOps Engineer responsible for designing and managing GitOps and CI/CD pipelines, release workflows, Terraform infrastructure, Kubernetes platforms, observability, cloud networking, security, and cost optimization. The role leads Azure-to-GCP migrations, administers cloud policies and access controls, supports production systems, mentors engineers, and drives Agile release and continuous-improvement practices.

Lead cloud migration and platform engineering efforts, designing AWS architectures and migration plans from on-prem VMware to AWS. Perform migration assessments, tooling selection, wave planning, dependency analysis, security/compliance alignment, OS and database troubleshooting, and optimize post-migration performance while collaborating with stakeholders and platform teams.

Architect and implement enterprise MLOps and LLMOps platforms on Google Cloud. Own model lifecycle pipelines, CI/CD automation, deployment, monitoring, drift detection, lineage, governance, and observability. Build GKE-based ML platforms, conduct architecture reviews, troubleshoot complex systems, collaborate with engineering and client teams, and mentor technical staff. Travel up to 30% is required.

Architect and build production-grade multi-agent AI systems, agentic workflows, RAG solutions, tool libraries, memory systems, and evaluation frameworks. Deploy scalable, low-latency solutions across AWS, GCP, or Azure using MLOps, CI/CD, observability, and infrastructure automation. Integrate and optimize LLMs, implement monitoring and safety controls, establish performance metrics, and lead technical delivery while mentoring team members and communicating complex AI concepts to stakeholders.

Designs and productionizes hybrid knowledge graph solutions for healthcare and clinical data using LPG and RDF technologies. Builds FHIR transformation and ingestion pipelines, implements ontology mappings, entity linking, identifiers, provenance, validation, and graph queries with Cypher or SPARQL. Collaborates with ontology, terminology, FHIR, clinical, and data engineering teams, while contributing to architecture reviews, code reviews, testing, and engineering best practices.

Leads delivery of large-scale AWS and digital transformation programs, overseeing planning, execution, reporting, risk mitigation, quality, and customer outcomes. Manages multi-functional teams of business analysts and engineers, develops product and service roadmaps, drives continuous improvement, and resolves customer issues. The role also involves people management, recruiting, coaching, business development, stakeholder communication, and aligning program portfolios with organizational strategy.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.