Applied AI Engineer

Project description

Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an applied AI engineer to build AI with real, measurable benefit across our operations and products.

Responsibilities

• Work closely with product teams to help define and execute technical criteria

  Find high-value uses for AI and define how success is measured

  Design, build and ship LLM-powered features and agents from prototype to production, such as automating client data onboarding

  Own retrieval and RAG over our data, building on the foundation our data engineers prepare

  Build evals and monitoring that track quality, cost and latency, and improve systems based on the results

  Help refine our customer-facing AI features

SKILLS

Must have

• Mid-to-senior software engineer who has shipped LLM-based features or agents to production, not only prototypes

  Production experience in Python (preferred) or Node.js/TypeScript

  Building with LLMs: prompt and tool design, structured outputs, retrieval/RAG and agent orchestration

  Evaluating AI systems: eval sets built from representative data, automated graders, human review and production monitoring

  AWS or another enterprise cloud platform, including its AI services (AWS Bedrock preferred)

  AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CD

  Strong English and communication skills

Nice to have

Agent tool protocols such as MCP

SQL and cloud warehouse data (Snowflake preferred)

Infrastructure as code (e.g. Terraform)

Exposure to energy, utilities or sustainability data