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

Lead design and delivery of Snowflake-centric cloud data platforms. Engage with clients to gather requirements, architect scalable secure solutions, provide technical leadership, develop roadmaps, advise executives, and guide implementation best practices across the analytics stack.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Lead Tableau data visualization strategy: design and optimize high-performance dashboards, establish governance and best practices, mentor teams, enable adoption, and partner with data teams for robust models and performance.

The Databricks Architect will design and implement data architecture solutions using Databricks, AWS, and Snowflake, focusing on MLOps and data governance.

Design and lead the development of cloud-native Generative AI platforms and solutions, ensuring scalability, security, and integration with enterprise applications.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

Lead portfolio-level Data & AI strategy and technical governance for enterprise clients. Advise C-suite, drive architecture and modern data stacks, win high-value engagements, and scale engineering and data science teams.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

Lead business development and growth in AI/ML, provide technical leadership in data science solutions, and enhance client engagement by delivering strategic data insights.