Principal Forward Deployed Engineer – AI & Developer Platform

Job description

**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. ## Requirements - Extensive software engineering experience: At least 17 years of total professional experience in technical software roles, including 10 or more years in software engineering and at least five years driving developer experience, API design, or AI platform architecture at scale. - Technical leadership: Proven ability to establish credibility with C-suite stakeholders, including CTOs and Chief AI Officers, while remaining hands-on with senior developers, platform engineers, and systems teams. - API design expertise: Advanced knowledge of RESTful API design, gRPC, OpenAPI specifications, API gateways, and enterprise integration patterns. - Kubernetes and cloud-native platforms: Deep understanding of Kubernetes internals, custom controllers, CRDs, operator patterns, and Cluster API (CAPI). - Developer workflows and automation: Experience implementing GitOps practices, continuous delivery pipelines, infrastructure-as-code automation, and developer self-service capabilities. - Generative AI and LLM infrastructure: Practical experience building or supporting production LLM and generative AI application stacks, including relevant orchestration frameworks and inference engines such as LangChain, LlamaIndex, Haystack, Semantic Kernel, Ray, vLLM, SGLang, and Triton Inference Server. - Data infrastructure: Familiarity with vector databases and search technologies such as Milvus, Qdrant, Pinecone, pgvector, and Weaviate, alongside streaming and caching technologies such as Kafka, Redis, Pulsar, Spark, and Flink. - GPU and accelerated computing: Experience integrating virtualized GPU workloads and technologies such as NVIDIA vGPU, Multi-Instance GPU (MIG), and GPUDirect RDMA into accessible platform capabilities. - Programming proficiency: Advanced skills in Go, Python, and TypeScript/Node.js for systems development, API microservices, custom controllers, and AI workflow integration. - Enterprise Kubernetes and infrastructure: Experience across enterprise Kubernetes distributions and managed services such as Amazon EKS, Google GKE, Azure AKS, OpenShift, and Rancher, with a strong understanding of application networking and storage abstractions. - Education: A bachelor's degree in Computer Science, Software Engineering, or a related technical discipline is preferred. Equivalent relevant professional experience may be considered in place of a degree. - Communication and collaboration: Excellent problem-solving, architectural communication, and stakeholder management skills, with the ability to translate complex customer needs into scalable engineering solutions. ## Benefits - Competitive annual base salary: CAD $115,600 to $185,000. - Annual performance bonus: Eligibility for a discretionary annual bonus, subject to applicable plan terms. - Equity compensation: Opportunity to receive a competitive new-hire equity grant and additional annual equity awards, subject to relevant plan documents and award agreements. - Health coverage: Medical, dental, and vision insurance plans. - Retirement savings: Registered Retirement Savings Plan (RRSP) participation, including employer matching contributions. - Employee Stock Purchase Program (ESPP): Opportunity to participate in a company share purchase program. - Employee assistance: Access to an Employee Assistance Program (EAP). - Paid time off: Company-paid holidays, vacation time, and paid sick leave. - Leave benefits: Applicable leaves of absence in accordance with relevant employment laws. - Remote work: Remote position based in Ontario, Canada. - Career and technical impact: Opportunity to influence enterprise AI infrastructure, developer platform architecture, and long-term product strategy while collaborating with experienced engineering teams.