AI/ML Engineer

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
Strong 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 apply
Apply 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.