Principal Forward Deployed Engineer - AI & Developer Platform

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Forward Deployed Engineer – AI & Developer Platform based in Canada . Take on a senior technical leadership role at the intersection of developer experience, cloud infrastructure, and artificial intelligence. You will work directly with complex enterprise customers to help engineering teams build, deploy, and scale AI-native applications on modern infrastructure platforms. Combining hands-on software development with architectural leadership, you will design APIs, SDKs, automation tools, and standardized workflows that simplify platform adoption. You will also help organizations operationalize large language models (LLMs), distributed AI workloads, and cloud-native applications in production environments. As a key connection between enterprise customers and core engineering teams, you will turn real-world challenges into product improvements and strategic roadmap priorities. This opportunity is ideal for an experienced technical leader who thrives on solving complex engineering problems while shaping the future of AI and developer platforms. Accountabilities - Developer Experience and API Architecture - Serve as the primary technical bridge between enterprise application developers, platform engineering teams, product managers, and senior technology leaders. - Design, validate, and standardize enterprise-grade REST, gRPC, and GraphQL APIs, Kubernetes Custom Resource Definitions (CRDs), and software development kits (SDKs). - Establish efficient developer workflows, continuous delivery pipelines, GitOps practices, and self-service developer portals using tools such as Argo CD and Flux. - Build command-line interfaces, testing frameworks, and automation tools that simplify adoption of Kubernetes and cloud infrastructure platforms.

- Define and promote consistent API design principles, developer documentation standards, and software quality practices across engineering teams. - AI Engineering and Platform Architecture - Architect production-ready infrastructure for real-time large language model inference, agent-based applications, and Retrieval-Augmented Generation (RAG) pipelines running on GPU-accelerated environments. - Standardize integration patterns for AI development frameworks and inference platforms, including LangChain, LlamaIndex, AutoGen, and vLLM. - Design scalable architectures for distributed AI workloads using technologies such as Ray, vLLM, SGLang, and Apache Spark. - Integrate vector databases, search systems, streaming technologies, and caching layers to support reliable and high-performance AI applications. - Establish approaches for exposing GPU infrastructure and accelerated computing capabilities through secure, developer-friendly APIs. - Hands-on Engineering and Product Strategy - Develop production-quality software using Go, Python, and TypeScript, including platform operators, custom controllers, API services, and AI integration components. - Contribute code and technical improvements directly to core platform products, translating field experience into reusable engineering solutions. - Identify recurring developer challenges, architectural limitations, and implementation workarounds, converting them into actionable product requirements and roadmap recommendations. - Collaborate with senior product and engineering leadership to influence long-term platform strategy and prioritize improvements that benefit enterprise customers. - Mentor engineers and forward deployed engineering colleagues, promoting technical excellence, effective API design, and high-quality implementation practices.