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

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.

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.

Lead strategic programs comprising multiple concurrent projects, aligning goals with organizational objectives. Manage program schedules, dependencies, budgets, risks, milestones, and performance metrics. Remove technical and operational blockers, coordinate cross-functional teams, escalate issues, and facilitate status meetings, executive dashboards, and program reviews. The role requires strong project and program management experience, SDLC and Agile methodology knowledge, and the ability to drive consensus across engineering teams.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

The Databricks Architect will design and implement large-scale data solutions, optimizing performance and reliability, while leading initiatives in data engineering.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Technology Partner will lead technology solutions and data transformation initiatives for Fortune 100 clients, providing thought leadership and managing executive relationships.

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.