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

Lead Replicant’s product design practice while remaining approximately 80% hands-on. Own the end-to-end enterprise customer experience, information architecture, design-system governance, research, prototyping, validation, and integration of product and conversation design. Influence cross-functional teams, establish design standards, guide hiring and development, and design AI and conversational experiences with strong failure recovery, trust, and usability.

Build and improve Replicant’s AI-native platform, including site reliability, CI/CD, developer tooling, observability, incident management, cloud infrastructure, and autonomous-agent harnesses. Own production infrastructure reliability at scale, reduce operational toil, improve deployment workflows, participate in on-call rotation, and shape platform engineering patterns. The role uses TypeScript/Node.js, Python, Terraform, Kubernetes, Helm, GCP, and modern monitoring tools.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.

Support pre-sales by owning technical engagements: gather requirements, design AI agent integrations, run hands-on trials, craft prompts, write/review APIs, analyze conversational data, and collaborate with sales, product, and engineering to demonstrate platform value.

Lead the quality engineering efforts for Replicant's AI platform by defining standards, improving testing strategies, and mentoring a QA team to ensure high-quality outputs across software systems.

Build and own production AI agents for assigned customers: discover workflows, design prompts and conversation flows, integrate with customer APIs/CRMs, create test sets, measure and harden agents, monitor live conversations, debug and ship fixes, and feed product improvements. Work customer-embedded to deliver reliable, secure, scalable LLM-powered voice and conversational AI solutions.

Design and deliver full-stack features for AI voice and chat products at enterprise scale. Integrate ASR/TTS and LLM improvements, optimize production systems for latency and cost, participate in on-call rotations, and collaborate with product and ML teams to ship high-impact, scalable solutions.

Lead a 8–15 engineer product squad as a servant leader: hire and grow engineers, own team budget and headcount, drive predictable high-quality delivery, embed AI practices, shape processes and outcomes, ensure engineering excellence (testing, validation, developer experience), and partner across Product and Delivery. Act as a peer in the engineering-management leadership team.