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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior/Staff Software Engineer, Agentic Platform - **Company:** Recruiting from Scratch - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $220,000.0 - $290,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Big Data, Code Review, Software Design Documents, Distributed Systems, Fault Tolerance, Reliability Engineering, Software Engineering, Datadog, Data Ingestion, Large Language Models, Multi-Agent Systems, Prompt Engineering, Backend, Agentic-AI, Evaluation of Large Language Models, Invoking Functions, Service Stack - **Published:** November 7, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-staff-software-engineer-agentic-platform-san-francisco-ca--c898f01a-c11d-43c5-b6d9-78057c829b33 ## About the Role Software Engineering, Software Engineering, Artificial Intelligence (AI), Reliability Engineering, Cost Control, Identify Issues, Manufacturing, Product Design, Funding, Memory Hardware, * 4+ years of professional software engineering experience. * Recent, hands-on experience building production agentic systems. * Experience with LLM orchestration, tool/function calling, multi-step reasoning, and multi-agent systems. * Understanding of retrieval, context engineering, memory, and MCP or comparable agent integration protocols. * Experience designing, building, and owning complex distributed systems. * Ideally, experience in data-intensive B2B SaaS environments. * Strong understanding of the modern agentic technology stack and the infrastructure required to operate LLM-driven systems reliably. * Experience building evaluation, testing, and observability frameworks for non-deterministic AI systems. * Ability to work with messy, heterogeneous, and unstructured real-world data and make it usable for AI-driven reasoning. * Strong system design and architectural decision-making skills. * Experience providing technical mentorship and guidance to other engineers without requiring formal management authority. * Ability to collaborate with Engineering and Product leadership to shape technical direction. * Strong ownership mentality and ability to lead complex technical initiatives from design through production. Technical Skills Agentic Systems and LLM Engineering * LLM orchestration. * Tool and function calling. * Multi-step reasoning. * Multi-agent architectures. * Agent memory and context management. * Retrieval and context engineering. * MCP or comparable protocols. * Agent evaluation and testing frameworks. * LLM observability and reliability guardrails. Distributed Systems and Backend Engineering * Distributed systems architecture. * Data-intensive backend systems. * Large-scale data ingestion and processing. * System design and infrastructure ownership. * Enterprise B2B SaaS platforms. * Reliability, scalability, and fault tolerance., * Experience building production agentic platforms or AI-powered enterprise products. * Experience with multi-agent orchestration and complex reasoning workflows. * Experience building AI systems that operate on large, fragmented enterprise datasets. * Background in data-intensive B2B SaaS. * Experience working with manufacturing, industrial technology, automotive, or other complex enterprise environments. * Experience building root-cause analysis, anomaly detection, or operational intelligence systems. * Experience with LLM evaluation frameworks, observability tooling, and production monitoring. * Experience designing systems where incorrect AI outputs can have significant operational or financial consequences. * Experience leading architecture discussions and influencing engineering direction. * Experience mentoring engineers and raising technical standards. Working Style and Culture * Strong technical ownership: Can independently own complex architecture and infrastructure decisions. * Hands-on leadership: Remains deeply involved in implementation while guiding technical direction. * Systems thinker: Understands how data ingestion, retrieval, reasoning, evaluation, and observability fit together. * Quality-driven: Understands that reliability and correctness are essential when AI systems influence consequential decisions. * Collaborative: Works effectively with Engineering and Product leadership. * Mentor-minded: Helps other engineers improve through design reviews, code reviews, and technical guidance. * Comfortable with ambiguity: Can help define architecture and best practices for a new and evolving engineering team. ## Description * Develop reliable reasoning infrastructure: Build and harden multi-step and multi-agent workflows, ensuring agents can reason effectively across large volumes of enterprise data. * Establish evaluation frameworks: Develop rigorous testing and evaluation processes for non-deterministic systems, measuring agent accuracy against expert-validated ground truth. * Measure real-world outcomes: Help evaluate system performance through meaningful customer outcomes, including detection rates, investigation speed, and warranty cost reduction. * Shape the technical roadmap: Partner closely with Engineering and Product leadership to define the architecture, technical vision, and roadmap for the agentic platform. * Improve engineering standards: Lead technical discussions, contribute to design documents, conduct code reviews, and establish best practices for building reliable agentic systems. * Mentor other engineers: Provide technical guidance and mentorship to engineers, particularly around agentic architecture, distributed systems, and system design.