Principal Forward Deployed Engineer – AI & Developer Platform
Principal-level forward deployed engineer embedded with large enterprise accounts in Broadcom's Infrastructure Software Division, building developer-facing APIs, SDKs, GitOps workflows, and LLM/AI platform infrastructure on Kubernetes (VKS/VCF). Writes production Go, Python, and TypeScript in the field and feeds learnings into the product roadmap.
- Compensation
- CA$115.6K–CA$185K / yr
- Location
- Ontario, Canada
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- 10+ years of software engineering experience
- 5+ years driving Developer Experience (DevEx), API design, or AI platform architectures at scale
- Technical credibility with C-suite leadership while staying hands-on with engineers
- Authority-level mastery of API design principles (RESTful design, gRPC, OpenAPI, API Gateway)
- Deep understanding of Kubernetes internals, custom controllers, CRDs, Operator patterns, and Cluster API (CAPI)
- Proficiency establishing GitOps workflows and infrastructure-as-code automation
- Hands-on experience with LLM/GenAI stacks (LangChain, LlamaIndex, Haystack, Semantic Kernel, Ray, vLLM, SGLang, Triton)
- Experience with vector databases and search (Milvus, Qdrant, Pinecone, pgvector, Weaviate)
- Experience with streaming and caching (Kafka, Redis, Pulsar, Spark, Flink)
- Experience integrating virtualized GPU workloads (NVIDIA vGPU, MIG, GPUDirect RDMA)
- Advanced proficiency in Go, Python, and TypeScript/Node.js
- Fluency across multi-cloud Kubernetes distributions (EKS, GKE, AKS, OpenShift, Rancher)
- 17+ years of total professional experience in technical software roles
Nice to have
- Bachelor's degree in Computer Science, Software Engineering, or related field (equivalent experience considered)
Full description
Job Description
We are seeking a Principal Forward Deployed Engineer to serve as a technical authority, enterprise developer advocate, and field-to-engineering leader across the Infrastructure Software Division (ISG). In this high-impact role, you will embed with our largest, most complex enterprise accounts to revolutionize how internal and external developer teams build, deploy, and scale AI-native applications on our core infrastructure.
As a Principal member of the core Engineering organization, you will operate at the intersection of Developer Experience (DevEx), API Architecture, and LLM/AI Platform Engineering. You will eliminate enterprise developer friction, author developer-facing APIs, SDKs, and reference implementations, and directly influence the multi-year technical roadmap for our AI and cloud-native software suite.
Unlike traditional pre-sales or post-sales roles, the Principal FDE functions as a senior extension of R&D in the field. Your mission is to establish world-class developer workflows, standardize LLM application delivery patterns, and build a high-bandwidth feedback loop that turns field innovations into platform features.
Key Responsibilities
- Divisional Field-to-Engineering Authority: Serve as the primary technical bridge between customer application developers, platform engineering teams, product managers, and C-level executive architects.
- API & SDK Architecture: Design, validate, and standardize enterprise-grade REST, gRPC, and GraphQL APIs, custom Kubernetes Custom Resource Definitions (CRDs), and software development kits (SDKs) to streamline platform consumption.
- Developer Workflows & GitOps: Establish end-to-end continuous delivery pipelines, GitOps patterns (ArgoCD, Flux), and self-service developer portals to accelerate software iteration cycles from months to minutes.
- Developer Advocacy & Tooling: Build developer CLI tools, testing harnesses, and automation frameworks to reduce cognitive load for internal application teams adopting our vSphere Kubernetes Service (VKS) and VMware Cloud Foundation (VCF) platforms.
- LLM Application Infrastructure: Architect production-ready environments for real-time LLM inferencing, Agentic frameworks, and Retrieval-Augmented Generation (RAG) pipelines on high-density GPU clusters.
- AI Developer Tooling & APIs: Standardize integration patterns for developer-facing AI frameworks (LangChain, LlamaIndex, AutoGen, vLLM) and expose clean, enterprise-compliant API gateways for model serving.
- Distributed AI Compute & Data Fabrics: Set technical execution patterns for containerizing, orchestrating, and tuning high-throughput AI runtime engines (Ray, vLLM, SGLang, Spark) alongside vector databases and caching layers.