Forward Deployed Engineer

Role overview

This is a senior embedded engineering role focused on leading the design, delivery, and adoption of production AI and ML systems inside client environments. The position centers on architectural ownership, model development, and solving integration challenges in live enterprise systems where reliability and security matter. The pace is fast, with production-ready AI typically shipped within roughly a month to six weeks.

Responsibilities

- Lead end-to-end design and implementation of complex ML/AI systems within client environments, owning architecture and driving work from research through scaled production - Build and evolve scalable ML platforms, pipelines, and infrastructure that fit inside existing client constraints - Diagnose and unblock integration challenges across unfamiliar codebases, cloud environments, and organizational contexts - Set the standard for AI-forward engineering practice and help both internal and client teams adopt modern AI-assisted tools effectively - Embed directly with client teams, earning trust quickly and translating business problems into AI system designs that actually work in their environment - Mentor and grow engineers, drive design reviews, champion responsible AI practices, and lead post-delivery adoption enablement

Requirements

- 7+ years of professional software engineering experience, including 4+ years on production AI/ML systems with deep hands-on generative AI work - Expert-level proficiency in Python or similar, with strong design instincts for scalable, maintainable systems - Deep cloud-platform expertise, including AWS services and AWS GenAI offerings - Proven track record shipping agentic systems in production, including within client or enterprise constraints - Mastery of AI frameworks and orchestration tools, plus experience building evaluation and observability for LLM applications - Strong understanding of AI safety, responsible AI principles, prompt injection defenses, and PII handling

Nice to have

- Experience with RAG pipelines: chunking strategies, embedding models, vector databases, and advanced retrieval techniques - API design experience at scale, plus cost optimization expertise such as token economics, caching strategies, model routing, and quantization - Working knowledge of Docker and Kubernetes for containerized deployments - Day-to-day, expert-level usage of AI-assisted coding tools such as Claude Code and Cursor

Benefits and work setup

Salary range: $176,612 – $243,680 CAD.