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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal AI Platform Engineer - **Company:** Everforth Apex - **Location:** Seattle, WA, United States (Remote available) - **Experience:** Experienced - **Salary:** $208,000 - $270,400 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Computing Platforms, Microsoft Azure, Encodings, Continuous Integration, Information Leak Prevention, Python (Programming Language), Routing, Performance Tuning, Role-Based Access Control, Regression Testing, Reliability Engineering, Azure Machine Learning, Software Deployment, AI Infrastructure, Enterprise Search, Graphics Processing Unit (GPU), Retrieval-Augmented Generation, Large Language Models, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Discretization, Pgvector, AI Platforms, LangSmith, Kubernetes, Bicep, LiteLLM, Machine Learning Operations, TensorRT, Hardware Infrastructure, Virtual Agents, VLLM, Terraform, Semantic Kernel, Api Management - **Published:** November 11, 2026 - **Apply:** https://www.dice.com/job-detail/1737e2e4-def6-4c08-8458-2f8ad5200e77 ## About the Role * 6+ years of experience in Platform Engineering, Site Reliability Engineering, Infrastructure Engineering, or related technical operations. * 2+ years running LLM/AI systems in production for real users, or significant ML platform operations experience. * Hands-on experience implementing and operating an AI/LLM gateway in front of one or more model providers. + Examples include LiteLLM, Azure APIM, Kong, Portkey, or similar. * Hands-on experience deploying open-weight models on GPUs. + Experience with vLLM, NVIDIA Triton/TensorRT-LLM, or comparable technologies strongly preferred. * Experience building and operating RAG solutions in production. * Experience with: + Vector stores + Embedding pipelines + Document processing + Azure AI Search, pgvector, or similar technologies * Experience measuring and managing AI/LLM costs by application, workload, or team. * Experience with AI observability, tracing, monitoring, and evaluation. * Strong hands-on engineering skills with: + Python + Terraform and/or Bicep + CI/CD + Kubernetes + AKS or comparable Kubernetes environments * Working knowledge of AI security risks, including prompt injection, data leakage, model abuse, access controls, and secure AI deployment practices. Preferred Qualifications * Azure cloud experience * On-premise GPU infrastructure experience * GPU sizing and optimization * Quantization * Fine-tuning / LoRA * MCP * LangChain / LangGraph * Semantic Kernel * AI agent frameworks and orchestration * Experience building and shipping AI applications * Experience using AI to improve or automate operational/support workflows * Experience working within a regulated enterprise environment * Experience establishing AI platform standards, governance, or enterprise frameworks What We're Looking For The ideal candidate has personally built and operated enterprise AI/LLM infrastructure in production and understands what happens after an AI solution moves beyond development and into ongoing operations. ## Description We are seeking a Principal AI Platform Engineer to help build, operate, and scale an enterprise AI platform supporting a growing portfolio of AI applications, agents, and business use cases. This is a highly hands-on Principal-level position with significant technical ownership and organizational influence. You will own critical components of the AI platform, including AI gateways, GPU/model serving, RAG, observability, cost management, security, governance, and production operations. This is an opportunity to join at an important stage of the organization's AI journey. You will help establish the technical standards, infrastructure, and operating model that future AI solutions are built against. What You'll Be Doing * Own key components of the enterprise AI platform from infrastructure through production operations. * Build and operate an AI gateway supporting multiple models/providers, including routing, fallback, security, quotas, and cost allocation. * Deploy and operate open-weight models on GPU infrastructure using technologies such as vLLM, NVIDIA Triton/TensorRT-LLM, or similar. * Build and operate production RAG systems, including vector stores, embedding pipelines, document processing, retrieval, and governed knowledge sources. * Establish AI observability and cost management, including visibility into token usage, GPU utilization, application/team consumption, performance, and overall platform cost. * Implement tracing, monitoring, evaluation, and regression testing using tools such as Langfuse, LangSmith, Arize, or similar platforms. * Deploy, monitor, troubleshoot, and support production AI agents and workflows. * Evaluate model performance and economics to determine the appropriate balance between managed cloud models and self-hosted/open-weight solutions. * Partner with engineering, infrastructure, security, operations, and business teams to operationalize AI safely and efficiently. * Help establish standards around AI security, model access, RBAC, guardrails, governance, and production readiness. * Provide technical guidance around model selection, routing, caching, batching, token optimization, and cost-versus-capability decisions. * Communicate technical recommendations effectively with engineering teams, architects, and senior leadership. * Remain hands-on with engineering and coding while providing Principal-level technical leadership., We're particularly interested in candidates who can speak specifically about: * Models they have deployed * GPU infrastructure they have personally worked with * Production scale and request volume * RAG architectures they have built * AI gateway architecture * Actual monthly AI/cloud spend * Cost optimization they personally implemented * Production incidents and how they resolved them * Users and workloads supported This position requires real production ownership. Candidates should be able to provide concrete examples, metrics, and lessons learned from systems they personally built or operated. Who May Not Be the Right Fit This likely will not be the right opportunity for candidates who are primarily: * Data Scientists without platform/infrastructure ownership * Application Developers without significant infrastructure experience * Engineers whose AI experience is primarily consuming model APIs * Architects who are no longer hands-on * Candidates whose AI experience is primarily personal projects or home labs * Candidates without experience supporting AI/ML systems in a true production environment Why This Opportunity Stands Out This is not a maintenance role within an already mature AI organization. The person selected will have an opportunity to influence the foundation and direction of an evolving enterprise AI platform. You'll combine hands-on engineering with platform ownership and have significant influence across: * AI infrastructure * Model strategy * Platform architecture * Cost optimization * Observability * Security * Governance * Production reliability * Operational AI adoption As the organization's AI capabilities continue to expand, there is also potential for the platform function and supporting team to grow over time. Location & Work Environment This is a remote position for candidates residing in an approved hiring state