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

Leads growth for the Insurance business through hyperscaler and data-platform partnerships. Builds partner relationships, identifies client opportunities, develops AI/ML proposals, expands accounts, and drives market growth. Translates client needs into enterprise data science and AI solutions, leads solution deployment, defines data science roadmaps, mentors technical teams, and strengthens AI/ML service offerings. Requires extensive Data and AI consulting, enterprise solution delivery, business development, and partner ecosystem experience.

Leads analytics consulting engagements using data science and advanced analytics to solve complex business problems. Manages project planning, stakeholder coordination, cross-functional delivery, and analytical solution development. Translates business requirements into actionable insights, presents recommendations to senior stakeholders, supports pricing analytics initiatives, and collaborates with technology teams on cloud-based analytics and data engineering solutions.

Develop and refactor Python optimization algorithms for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, analyze large datasets, design experiments, monitor KPIs, and provide data-driven recommendations. Collaborate with business stakeholders and data science teams to deliver analytical solutions, while coaching colleagues and applying advanced statistical and operations research methods.

Leads client-facing AI and Generative AI engagements from discovery through production while combining project leadership with hands-on engineering. Responsibilities include architecting and deploying LLM applications, RAG systems, AI agents, and production AI services; coordinating technical teams; managing delivery risks and timelines; and translating business needs into practical AI solutions. The role requires expertise in Python, cloud platforms, LLM frameworks, vector databases, APIs, microservices, deployment, evaluation, monitoring, and responsible AI.

Designs and deploys advanced AI and machine learning solutions, including AI agents, RAG systems, analytical models, and data products on Google Cloud Platform. Responsibilities include preparing structured and unstructured data, developing AI agent tools, applying NLP and deep learning, collaborating with customers and stakeholders, generating business insights, and communicating analytical results.

Leads complex enterprise AI programs from inception through production deployment. Coordinates data science, machine learning, data engineering, product, architecture, and client teams while overseeing RAG pipelines, multi-agent AI ecosystems, and knowledge graphs. Manages risks, dependencies, technical challenges, stakeholder communication, delivery progress, and strategic outcomes across globally distributed teams.

Designs and prototypes scalable agentic AI architectures across Azure, AWS, or GCP. Responsibilities include multi-agent orchestration, LLM applications, RAG pipelines, memory and vector search, enterprise integrations, cloud-native engineering, code reviews, AI governance, security guardrails, and stakeholder leadership. The role requires hands-on development, framework evaluation, and translating business needs into production-ready AI platforms.

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Deploys, integrates, and scales enterprise Generative AI solutions across Azure environments. The role develops LangGraph agentic workflows, fine-tunes and evaluates LLMs, reduces hallucinations, integrates retrieval systems and APIs, and collaborates with customer engineering teams. Responsibilities also include requirements documentation, process mapping, automation engineering, software architecture, and communicating business value.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.