Principal Product Manager, AI Platform

Company Overview

See https://gigawatt.ai

Job Summary

We are seeking a Principal Product Manager for Gigawatt’s AI Platform team, the group building the agent infrastructure that utilities build on, on top of our common data fabric. This platform is not an internal enabler. It is the product we put in our customers’ hands, and it is how a utility goes from buying software to building the agents its own operation needs.

Your focus is the agent layer. Our data fabric already unifies context across utility source systems; you own the tools that turn that context into agents which are secure, explainable, and auditable by default. Utilities operate critical infrastructure under regulatory scrutiny, so “why did the agent do that?” must always have an answer, and the answer has to satisfy their auditors, not just ours. Trust is not a feature you add later; it is the product.

There are two paths onto the platform and you own both. Today a utility’s developers build in code against our SDK. Our goal is that a utility SME can build, test, and ship an agent without writing any code. Deciding what gets abstracted, what stays code, and how fast we move between the two is the central product judgment of this role. Gigawatt’s own product teams build on the same rails, which is how we prove the platform before a customer has to.

Reporting to the SVP of Product, you’ll partner closely with AI engineers, platform and data engineering, security, design, and our first utility customers to:

• Turn the data fabric into agent-ready context: retrieval, grounding, and the permissioned surface over meter, outage, billing, asset, and work management data that agents reason and act on safely.
• Build the agent toolkit: orchestration, retrieval, tool and action APIs, evaluation harnesses, guardrails, and observability, exposed both as an SDK and as a builder a non-developer can use.
• Make trust native: per-tenant isolation, permissioning, data lineage, decision traces, human-in-the-loop controls, and audit trails a utility can hand to its own regulator.
• Treat the platform as a shipped product: real documentation, versioning, SLAs, a support model, and adoption metrics. Customers will depend on it in production.

Key Responsibilities

• Own the AI platform roadmap: Drive strategy and roadmap for the agent infrastructure built on our data fabric, and make the build-versus-buy calls across a fast-moving AI tooling landscape.
• Own both build paths: Define the SDK and code-first experience utilities use today, and drive the no-code builder that lets their SMEs ship agents themselves. Decide what gets abstracted, what stays code, and how the two converge over time.
• Ship with design partners: Define high-value MVPs, build them with real utility customers, and iterate fast on the friction and edge cases that only show up in someone else’s environment.
• Own developer and builder experience: API and SDK design, documentation, sandboxes, templates, onboarding, and the paved path from first agent to production deployment.
• Own evaluation and observability: Define how agent quality is measured before and after release: golden sets, regression suites, online evals, and the trace and audit records a utility or regulator can inspect. Give customers visibility into their own agents, not just internal dashboards. Make the build-versus-buy call across LangSmith, Arize, and open standards, and own the decision on where trace data lives.
• Design for human-in-the-loop: Define where agents act autonomously, where they pause for approval, and how state, interrupts, and replay work in practice. In utility operations the escalation path is a product decision, not an implementation detail.
• Own trust, tenancy, and governance: Explainability, auditability, per-tenant data isolation, customer-controlled retention, least-privilege access, and safe autonomy, especially for agents a non-developer built.
• Own the commitments that come with shipping externally: Versioning, backwards compatibility, deprecation policy, SLAs, the support model, and the product input into packaging and pricing.
• Execute: Drive with engineering and design to release production-ready capabilities on compressed timelines. Drive the product lifecycle by defining use cases and technical requirements, collaborating on data model and API design, and conducting product testing to ensure key requirements are met.
• Own outcomes: Set KPIs (customer agents live in production, time-to-first-agent, share of agents built without code, eval pass rates and regression escapes, agent task success and escalation rate, latency and cost per task, support burden) and manage dependencies across the platform.
• Wear many hats: Do whatever it takes to move the platform forward, and take on new areas of ownership as priorities evolve.

Utility experience is a plus but not a requirement. We have deep domain expertise in house and will teach it.

• Platform experience: 8+ years in product management, including 2+ years on AI or ML systems, and significant time on a platform or product that customers outside your own company built on top of.
• External platform products: You have shipped APIs, SDKs, or an application builder that customers depended on in production. You have lived versioning and deprecation, support escalations, security questionnaires, and the difference between an internal tool and a product someone signs a contract for.
• AI systems depth and eval judgment: Working fluency in modern AI systems: LLMs, retrieval, tool use, guardrails, and the cost, latency, and reliability tradeoffs that come with them. You can read a trace and tell whether a failure was retrieval, prompt, tool schema, or model, you have designed eval sets, and you have shipped against a quality bar rather than a demo. You can hold your own in an architecture review and write a technical spec engineers respect. You do not need to write production code.
• Regulated, multi-tenant environments: You have shipped software into an environment where someone outside your company audits what it did: financial services, healthcare, energy, or public sector. You have a real point of view on tenant isolation, explainability, data lineage, retention, and least-privilege access, and you treat security and compliance review as a design input rather than a launch blocker.

• Making power safe for non-developers: Experience with low-code, configuration-driven, or builder-style products, and a point of view on how to give a business user genuine capability without giving them a way to cause harm. Close to the heart of the role, and the strongest differentiator among otherwise comparable candidates.
• Agent orchestration: Hands-on familiarity with a modern agent framework, ideally LangGraph or LangChain: graph-based orchestration, state and checkpointing, tool and schema design, and multi-agent handoffs.
• Cloud AI platforms: Experience building on a managed model platform, ideally AWS Bedrock, including model selection tradeoffs, guardrails, throughput and cost management, and VPC, IAM, and data residency constraints.
• Data fluency: You will build on our existing data fabric rather than define it, but comfort reasoning about data models, integration across messy enterprise source systems, and streaming versus batch helps you know what the fabric can and cannot answer for an agent.
• Domain and background: Utility or energy sector exposure, or a background in software engineering, ML, or data engineering before moving into product.
• Tools: Experience with Jira, Figma, and standard product management tools.

• Remote: prioritizing central and eastern timezones (ideally Chicago, Austin, or Nashville).

• Competitive salary and equity.
• The chance to shape a transformative AI product in a vital industry with a rock-star team.
• Comprehensive benefits: health insurance, remote flexibility, and 401k match.
• If you are passionate about modernizing the systems that run America’s utilities, apply today!