Applied AI Engineer

Companion.energy connects the financial and operational sides of energy management for large enterprises and energy players. The Applied AI Engineer will build and operate the architecture, evaluation systems, tool surfaces, model strategy, and guardrails that support reliable agentic workflows, while also delivering AI features for energy contract and hedge position ingestion.

Responsibilities:

• The agentic architecture. How an agent is defined, how tools are exposed to it, how context gets assembled, how a run is traced, and how state and long-running work are handled. Today each of our AI workflows answers those questions its own way. You decide how we answer them once, so that building the next agentic feature is routine rather than a new invention
• Evaluation. Deciding what "good" means for a feature whose output differs on every run, then building the harnesses and datasets that hold it to that standard and catch a regression before a customer does. This is the part most teams skip and the part that separates a demo from a product
• The tool surface. We serve the platform to agents over MCP today, through an internal management server and a customer-facing analytics server. Designing tools to be used by models rather than people is real design work, and so is deciding which steps of a workflow should stay deterministic code instead
• Multi-model. Which model runs which step, how we stay able to swap them as they change, and how that decision gets made on evaluation evidence rather than on release notes
• Cost, latency and reliability as product constraints. A budget per feature, caching, retries and fallbacks, and the monitoring that tells us when a workflow has become slower or more expensive than the value it delivers
• Guardrails our customers can accept. Permission-aware, tenant-isolated and auditable, with human-in-the-loop wherever a wrong number turns into a claim against a supplier. Our customers are large enterprises, and their data cannot leak across tenants or into the wrong hands
• The workflows on top. You will build features as well as foundations, and the foundation gets judged by how fast the next feature ships. Contract and hedge position ingestion is next in line, following the pattern reconciliation established: take the document, parse it, propose the mapping, let a human confirm