This is more than a full-stack role — it's your chance to build the AI layer of the platform the world's infrastructure runs on. Sitetracker is building Scout, our AI product line: production LLM systems and agentic workflows embedded in the software companies like Cox, Telefónica, and EVgo use to deploy critical infrastructure. As a Product Engineer, you'll take AI capabilities from idea to production agent orchestration and model integration on the backend, and the web and mobile experiences that put them in customers' hands.
This is shipping real, customer-facing AI , not prototypes or research demos. You'll own features end-to-end, work with frontier models (Anthropic, OpenAI), and see your work land in the hands of enterprise users deploying telecom networks, EV chargers, and renewable energy around the world.
As a Product Engineer on Scout, you'll hit the ground running and ship customer-facing AI from day one. You'll design, build, test, deploy, and monitor AI product features end-to-end , building agentic LLM systems with multi-agent workflows, tool/function calling, RAG pipelines, and prompt engineering over frontier models from Anthropic and OpenAI.
You'll work across the full stack: backend services in Python, Java, Node.js, or Go, and TypeScript/React frontends spanning both web and mobile app experiences. You'll make AI dependable by building automated evaluation pipelines, guardrails, and testing strategies for non-deterministic outputs — and running it on production infrastructure: scalable, fault-tolerant services on AWS and Kubernetes.
You'll think like a product engineer, not a ticket-taker: dig into user needs, absorb the problem space, make smart calls about what to build, keep stakeholders informed, and find ways to unblock yourself.
Full-Stack Product Engineering
- Build production backend services in one or more of Python (FastAPI or similar), Java, Node.js, or Go.
- Ship production web interfaces in TypeScript and React; mobile app development (React Native or similar) is a plus.
- Design REST APIs and data models across relational and NoSQL databases.
- Own features end-to-end: implementation, testing, deployment, and monitoring.
AI / LLM Engineering
- Build with LLM APIs (Anthropic, OpenAI): agentic workflows, tool/function calling, RAG pipelines, and prompt engineering. Production experience preferred; substantial personal or open-source projects considered.
- Write evaluation pipelines and testing strategies for non-deterministic AI outputs.
- Bonus: agent frameworks (LangChain, LangGraph, LlamaIndex, CrewAI), Model Context Protocol (MCP), LLMOps tooling (LangSmith, Langfuse), vector databases, or model serving (vLLM, GPU inference).
Production Systems & AI Infrastructure
- Deploy and operate services on AWS with Docker, Kubernetes, and CI/CD (Scout runs on AWS).
- Instrument systems with observability and handle customer data securely in AI systems.
- Design fault-tolerant, scalable services that serve AI workloads reliably.
- Salesforce platform and Apex — Scout integrates with the Sitetracker Salesforce platform.
Product Thinking & Communication
- Dig into user needs and use that context to decide what to build and how.
- Explain technical decisions and AI concepts clearly to technical and non-technical audiences.
- Break challenges into iterative solutions, keep stakeholders informed, and unblock myself.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
