Forward Deployed Engineer
Robots and Pencils1 day ago
Remote, CanadaStaff+
Responsibilities
- Lead end-to-end design and implementation of complex ML/AI systems within client environments, from research through production.
- Build, deploy, and evolve scalable ML platforms, pipelines, and infrastructure for reliable model development and deployment.
- Diagnose and resolve integration challenges across unfamiliar codebases, cloud environments, and organizational contexts.
- Embed with client teams, translate business problems into practical AI system designs, and drive adoption after delivery.
- Partner with product, engineering, and leadership teams to align technical direction with business outcomes.
- Lead design reviews, technical discussions, and engineering standards while communicating AI tradeoffs to technical and non-technical stakeholders.
- Develop AI architecture, responsible AI practices, enablement materials, and client engineering capabilities.
- Mentor junior and mid-level engineers through coaching, code reviews, and pairing, while owning ambiguous and high-stakes production work.
Requirements
- 7+ years of professional software engineering experience, including 4+ years focused on AI/ML systems in production.
- Expert software engineering background using Python or a similar language, with strong scalable-systems design skills.
- Deep expertise with cloud platforms, including AWS services and AWS generative AI offerings.
- Proven experience designing and shipping complex agentic systems in production and enterprise client environments.
- Mastery of AI frameworks and orchestration tools, plus experience with LLM evaluation frameworks and observability tools.
- Strong understanding of AI safety, responsible AI, prompt injection defenses, and PII handling.
- Extensive experience building RAG pipelines involving chunking, embedding models, vector databases, and advanced retrieval techniques.
- Experience designing and integrating internal and third-party APIs at scale.
- Advanced expertise in AI cost optimization, including token economics, caching, model routing, and quantization.
- Strong working knowledge of Docker and Kubernetes for containerized deployments.
- Expert day-to-day use of AI coding tools such as Claude Code and Cursor.
- Ability to operate effectively in ambiguous, fast-moving client environments and build trust with engineering stakeholders.
Tech Stack
Categories
Forward Deployed