AI Engineer

Our client is seeking an AI Engineer to design, build, and product ionize agentic AI systems, LLM applications, and RAG pipelines that improve production operations, reliability, automation, and governance. (Hybrid, Toronto, 3 days on site/wk)

• 5+ years of software development experience in one or more languages such as Python, C/C++, Go, or Java.
• Strong hands-on experience building and maintaining large-scale Python applications.
• 3+ years of experience designing, architecting, testing, and launching production ML systems.
• Experience with model deployment and serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
• Practical experience with Large Language Models, including API integration, prompt engineering, fine-tuning or adaptation, RAG, and tool-using agents.
• Experience building agentic AI systems using vector retrieval, function calling, secure tool execution, structured reasoning, and guardrails.
• Understanding of commercial and open-source LLMs such as OpenAI, Gemini, Llama, Qwen, and Claude.
• Solid understanding of applied statistics, core machine learning concepts, algorithms, and data structures.

• Proficiency building and operating cloud infrastructure, ideally AWS.
• Experience with AWS containerized services such as ECS or EKS.
• Experience with serverless technologies such as AWS Lambda.
• Experience with AWS data services such as S3, DynamoDB, and Redshift.
• Experience with orchestration tools such as AWS Step Functions.
• Experience with model serving platforms such as Sage Maker.
• Experience with infrastructure-as-code tools such as Terraform or Cloud Formation.

• Design and implement tool-calling agentic AI systems that combine retrieval, structured reasoning, and secure action execution.
• Build AI agents using function calling, change orchestration, policy enforcement, and MCP protocol patterns.
• Engineer robust safety guardrails to support compliance, governance, and least-privilege access.
• Build evaluation frameworks for open-source and foundational LLMs.
• Develop retrieval pipelines, prompt synthesis, response validation, and self-correction loops for production operations.
• Connect AI agents to observability, incident management, and deployment systems.
• Enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
• Partner with production engineers and application teams to translate operational pain points into agentic AI roadmaps.
• Define objective functions tied to reliability, risk reduction, cost optimization, and business-aligned outcomes.
• Build validator models, adversarial prompts, policy checks, deterministic fallbacks, circuit breakers, and rollback strategies.
• Instrument continuous evaluations for usefulness, correctness, and risk.
• Optimize cost and latency through prompt engineering, context management, caching, model routing, and distillation.
• Use batching, streaming, and parallel tool calls to meet stringent SLOs under real-world production load.
• Build and maintain RAG pipelines, including domain knowledge curation, data-quality validation, feedback loops, and knowledge freshness frameworks.
• Drive design reviews, rigorous experimentation, and high-quality engineering practices.
• Mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns.