Senior AI Engineer – LLM / Agents

Do you want to work with high caliber professionals in a dynamic and growing company? Are you entrepreneurial, hard-working, and collegial? Join us at SB Energy, a leading company backed by SoftBank and Ares pairing cutting-edge innovation with best-in-class execution. Our Mission is to provide reliable, affordable energy at scale to support America’s growing energy demands. Headquartered in Redwood City, CA, SB Energy develops, builds, owns & operates some of the largest and most technically advanced energy and data center infrastructure projects in the United States. Since launching in 2019, the company has rapidly grown into a top-tier integrated platform with over 3 gigawatts (GW) in operation and a multi-GW pipeline of energy and data center infrastructure nationwide. SB Energy also utilizes its strong culture of innovation to identify and incorporate new technology into our projects, including our AI-based digital platform, to deliver energy infrastructure that is local, reliable, and matched to load. We are building the energy and technology future—today. Come join us in accelerating the energy transition to cleaner, more sustainable sources of power! Title: Senior AI Engineer – LLM / Agents Basic Function: Senior AI Engineer – LLM / Agents  will serve as the technical principal for how SB Energy designs, measures, observes, and scales AI-enabled workflows. This role defines how ChatGPT, Codex, custom agents, MCP servers, skills, tools, APIs, structured responses, token and context management, traces, evals, and production observability work together in governed enterprise workflows. This person will establish reusable architecture, standards, templates, and quality gates so agentic workflows can move from prototype to controlled production use. The role will help SB Energy make workflows measurable, observable, secure, cost-aware, reliable, and auditable across Power Markets, Storage Optimization, Asset Operations, Project Development, Engineering, Finance, HR, and other approved enterprise domains. The AI Engineer owns the cross-domain operating method, evaluation system, observability patterns, and reusable technical standards that allow those workflows to scale with quality. Responsibilities: - Define the technical architecture for SB Energy agent workflows, including ChatGPT, ROCstar, MCP, agents, skills, tools, APIs, retrieval, structured outputs, trace metadata, evals, and observability. - Establish reusable workflow patterns for agent planning, tool calling, data retrieval, exception handling, fallback behavior, escalation, and production support. - Create standards for skill design, prompt structure, tool descriptions, tool schemas, API response formats, source citation behavior, structured outputs, and workflow handoffs. - Define token and context management practices, including when to use conversation state, retrieval, file search, cached context, summarization, structured intermediate state, and compressed tool returns. - Build evaluation frameworks for agent workflows, including gold datasets, regression tests, numerical reconciliation checks, rubric-based grading, tool-call correctness checks, and human feedback loops. - Lead the design of observability for agents and tools, including workflow logs, cost, latency, token usage, tool success rate, bad-answer rate, eval pass rate, user acceptance, and incident tracking. - Partner with domain Forward Deployed Engineers to convert high-value workflows into measurable agent systems with explicit inputs, outputs, owners, permissions, evals, runbooks, release gates, and continuous improvement loops. - Partner with the Enterprise Systems and Agent Platform role on MCP servers, connector reliability, RBAC, secrets, audit logging, deployment patterns, API governance, and production platform readiness. - Review agent workflow designs before production release and define go/no-go criteria for quality, safety, reliability, cost, latency, security, and operational support. - Create reusable templates for agent specifications, skill specifications, eval protocols, workflow scorecards, incident reviews, production readiness checklists, and workflow-level success metrics. - Mentor FDEs, analysts, engineers, and implementation partners on eval-driven development, tool-based workflow design, observable agent operations, and production-quality AI systems. - Identify recurring failure modes across agents and tools and turn them into tests, standards, instrumentation, documentation, and platform improvements. Qualifications/Requirements: - Bachelor's degree in Computer Science, Software Engineering, Data Science, Machine Learning, Information Systems, or a related technical field required. - Master's degree in Computer Science, AI/ML, Data Systems, Software Engineering, or a related field preferred. - 10+ years of experience in software engineering, data science, machine learning systems, enterprise AI, platform engineering, or decision systems, with demonstrated technical leadership across complex production workflows. - Experience designing, building, or governing agentic AI systems, LLM applications, tool-calling workflows, evaluation systems, observability systems, or production AI platforms. - Strong understanding of modern LLM workflow design, including prompts, skills, tools, retrieval, structured outputs, APIs, orchestration, context management, token economics, and human-in-the-loop controls. - Strong engineering judgment around production readiness, failure modes, logging, testing, monitoring, cost, latency, security, permissions, and operational support. - Experience building eval datasets, regression tests, model or agent scorecards, acceptance criteria, telemetry dashboards, release gates, and production quality standards. - Experience with Python, APIs, data pipelines, cloud platforms, CI/CD, logging and monitoring systems, analytics workflows, and enterprise integra