Role snapshot

- Title: Senior AI Engineer
- Location: Toronto, ON (hybrid, 2–3 days on-site)
- Engagement: Full-time, leading AI delivery on Augmented enterprise client projects
- Level: Senior (6+ years of experience, 2+ years building production AI systems)
- Reports to: Head of AI Engineering

About the role

Augmented is hiring a Senior AI Engineer to lead the design and delivery of enterprise-grade AI systems for large clients in regulated industries. You will set technical direction, architect LLM and agent-based solutions, and make sure they are reliable, secure and cost-effective in production.

You will be the senior technical voice with client stakeholders and a mentor to the engineers around you.

Key responsibilities

- Architect end-to-end AI solutions: RAG platforms, multi-agent systems, AI assistants and workflow automation
- Make build-vs-buy and model selection decisions, balancing quality, latency, cost and data residency
- Define evaluation strategy, quality gates and red-teaming for LLM systems before and after release
- Set engineering standards for prompts, agents, tool use, observability and LLMOps across projects
- Design for security, privacy and compliance: PII protection, access controls, audit trails and responsible-AI policies
- Lead production rollouts, scaling, performance tuning and cost optimisation
- Translate business goals into AI roadmaps and present options and trade-offs to client technical and executive leaders
- Lead and mentor a team of AI engineers through design reviews, pairing and code reviews
- Contribute to pre-sales solutioning, estimates and proposals with Augmented leadership

Required qualifications

- 6+ years of software or ML engineering experience, including 2+ years delivering production LLM or GenAI systems
- Expert Python and strong software architecture skills (APIs, distributed systems, event-driven design)
- Deep experience with RAG,



agents and tool use using frameworks such as LangGraph, LlamaIndex or custom orchestration
- Proven record designing LLM evaluation, monitoring and guardrails at scale
- Solid cloud architecture skills on Azure, AWS or GCP, including Kubernetes, IaC and CI/CD
- Experience leading technical teams and working directly with senior client stakeholders
- Legally entitled to work in Canada

Nice to have

- Fine-tuning, distillation or self-hosting of open-source models (vLLM, Hugging Face)
- Model Context Protocol (MCP) and enterprise agent integrations
- Classical ML and MLOps (MLflow, feature stores, model serving)
- Data platform experience with Databricks or Snowflake
- Experience in Life Sciences (GxP, clinical, commercial) or Financial Services (risk, fraud, compliance)
- Published work, open-source contributions or conference talks in AI
- Consulting or client-facing delivery background

Tech stack

- Languages: Python, SQL, TypeScript
- AI: LLM APIs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock), open-source models
- Orchestration: LangGraph, LlamaIndex, MCP, custom agent frameworks
- Retrieval: vector databases, hybrid search, re-rankers, knowledge graphs
- Serving & LLMOps: vLLM, MLflow, LangSmith / Langfuse, eval harnesses
- Cloud & DevOps: Azure or AWS, Kubernetes, Terraform, CI/CD
- Data: Databricks, Snowflake

Why Augmented

Augmented is an AI-native engineering delivery hub serving large enterprises in Life Sciences and Fintech, with teams in Toronto and the US.
- Shape AI strategy and architecture for major enterprise clients
- Small, senior teams with direct access to client decision-makers
- Lead AI-native delivery practices and modern tooling

How to applyApply through this posting with your resume and a short note on an AI system you architected and led to production (scale, design choices, results).
- Augmented is an equal opportunity employer. Accommodations are available on request at any stage of the hiring process.

📌 AI/ML Engineer (Toronto)
🏢 Augmented
📍 Toronto