Senior Staff Inbound Product Manager responsible for owning the roadmap, vision, and requirements for conversational AI features within ServiceNow's AI platform. The role requires translating customer feedback into product improvements, guiding product strategy across multiple teams, and steering the development of AI features including disambiguation, clarification, document/multimodal experiences, slot filling, feedback intelligence, and fallback handling.… Success requires 12+ years of enterprise product management experience, deep knowledge of conversational AI, generative AI, and agentic systems, plus hands-on experience with AI evaluation, prompt engineering, retrieval systems, and knowledge bases. The person will create PRDs, define testing and quality requirements, support strategic customers through pilots and go-lives, and coordinate dependencies across engineering and design teams.
context-engineering jobs in San Diego
As a Software Architect, you will design AI-first software development lifecycles and lead teams in building GenAI-integrated platforms. You'll architect solutions using prompt engineering, RAG, embeddings, and agentic AI while managing multi-developer environments where AI agents and engineers collaborate.… You'll own product architecture, prototype AI-driven solutions with modern DevOps and observability, and coach teams on AI-accelerated development practices. The role requires 6+ years of production software experience, hands-on use of agentic IDEs, proven GenAI integration delivery, and strong architecture expertise across APIs, microservices, cloud platforms, and CI/CD.
Lead the design and engineering of enterprise-ready AI agents with retrieval, orchestration, policy-based routing, and lifecycle observability across multiple AI providers. Develop cloud-native abstraction layers, containerized microservices, and serverless architectures to deliver scalable agentic systems.… Tailor domain-specific workflows for finance, healthcare, and retail verticals through intelligent automation. Conduct design workshops, POCs, and code sessions with clients to shape data-driven agent workflows while defining metrics, evaluation harnesses, and best practices for agent accuracy, latency, safety, and cost-effectiveness.
Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers.… Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.