Applied AI Engineer
- Canada
Full-time
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Job details
- Job type
- Full-time
- Posting language
- English
- Posted
Original job description
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
Mandatory Skills Description:
- 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 Skills Description:
- 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
Languages:
- English: B2 Upper Intermediate
More about this job
Responsibilities
Identify valuable AI use cases, define success measures, and build and ship production LLM features and agents, including automated client data onboarding. Own retrieval and RAG over prepared data, develop evaluation and monitoring systems for quality, cost, and latency, and collaborate with product teams to improve customer-facing AI features.
Requirements
Requires mid-to-senior software engineering experience, including production delivery of LLM-based features or agents and production experience with Python or Node.js/TypeScript. Candidates should have experience with LLM design, retrieval and RAG, agent orchestration, AI evaluation and monitoring, enterprise cloud AI services, and AI-native development workflows; strong English communication is also required.
Skills
- Python
- Node.js
- TypeScript
- Large Language Models
- Prompt Design
- Tool Design
- Structured Outputs
- Retrieval-Augmented Generation
- Agent Orchestration
- AI Evaluation
- Production Monitoring
- AWS
- AWS Bedrock
- PostgreSQL
- Snowflake
- CI/CD
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Energy
- Data & Analytics
- Engineering
Keywords
- Applied AI
- Large Language Models
- AI Agents
- Python
- Node.js
- TypeScript
- Ruby On Rails
- PostgreSQL
- Snowflake
- AWS
- AWS Bedrock
- Prompt Design
- Tool Design
- Structured Outputs
- Retrieval-Augmented Generation
- Agent Orchestration
- AI Evaluation
- Automated Graders
- Human Review
- Production Monitoring
- CI/CD
- Model Quality
- Latency
- Cost Optimization
- MCP
- SQL
- Terraform
- Energy
- Renewable Energy
- Data Onboarding