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Applied AI Engineer (Remote Canada)

DXC Luxoft

Nouveau Remote
Remote Senior 🇬🇧 English
Python Node.js TypeScript Ruby on Rails PostgreSQL Snowflake AWS AWS Bedrock RAG LLM CI/CD Terraform

Description du poste

About the role

An energy solutions provider is hiring an Applied AI Engineer remotely in Canada. Its AI-enabled platform supports operations, tracking and decision-making for large-scale renewable and traditional energy portfolios, and is delivering a key client MVP in February 2027, continuing into 2028. The stack is Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS.

Key responsibilities

  • Work closely with product teams to 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 the data, building on the foundation prepared by data engineers.
  • Build evals and monitoring that track quality, cost and latency, and improve systems based on the results.
  • Help refine customer-facing AI features.

Required profile

  • Mid-to-senior software engineer who has shipped LLM-based features or agents to production, not just prototypes.
  • Production experience in Python (preferred) or Node.js/TypeScript.
  • Experience building with LLMs: prompt and tool design, structured outputs, retrieval/RAG and agent orchestration.
  • Experience evaluating AI systems: eval sets from representative data, automated graders, human review and production monitoring.
  • 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.

Required skills

  • AWS or another enterprise cloud platform, including its AI services (AWS Bedrock preferred).
  • 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, and other languages.

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