> Markdown version of [/jobs/ext/4084473-forward-deployed-engineer-enterprise-context-layer-agentic-data-cloud](https://www.wearedevelopers.com/jobs/ext/4084473-forward-deployed-engineer-enterprise-context-layer-agentic-data-cloud). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Forward Deployed Engineer, Enterprise Context Layer, Agentic Data Cloud - **Company:** Google LLC - **Location:** Kirkland, WA, United States - **Experience:** Experienced - **Salary:** $174,000 - $252,000 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Business Logic, Computer Vision, Cloud Computing, Graph Database, Statistical Hypothesis Testing, Open Source Technology, Software Engineering, Large Language Models, Multi-Agent Systems, Llamaindex, Generative AI, Agentic-AI, Model Context Protocol - **Published:** October 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=17ce3ba844dde692 ## About the Role * Bachelor's degree or equivalent practical experience. * 5 years of experience in software development and testing or launching software products. * 3 years of experience with software design/architecture. * Experience with Generative AI (GenAI) concepts (e.g., Large Language Model (LLM), multi-modal, large vision models)., * Experience with Knowledge Graph, Ontologies. * Experience developing with Generative AI and Large Language Models. * Familiarity with open-source AI orchestration frameworks (e.g., LangChain, LlamaIndex). Experience with agentic architectures, tool usage, or the Model Context Protocol (MCP). * Background in automated evaluation and benchmarking pipelines * Ability to quickly understand customer needs in new domains, and form and test hypotheses on technical solutions and evaluations to improve model/agent outcomes to meet needs. ## Description * Analyze customer industries and profiles to translate business logic into functional AI agents. * Identify necessary agent skills and APIs, map decision trees, and build system guardrails to ensure accurate outputs. * Build custom skills or integrations to assist customers, and document successful processes for future standardization. * Tune AI agents by providing the appropriate voice, tools, and specific knowledge base required for specific brands.