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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineer - **Company:** Bright Vision Technologies - **Location:** Raleigh, NC, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Applications Architecture, User Authentication, Microsoft Azure, C++ (Programming Language), Cloud Computing, Cloud Engineering, Program Optimization, Nvidia CUDA, Computer Programming, Computer Engineering, DevOps, Distributed Systems, Memory Management, Python (Programming Language), Machine Learning, Open Source Technology, Azure Machine Learning, System Programming, Management of Software Versions, AI Infrastructure, Data Logging, Google Cloud, Cloud Platform System, Autoscaling, Istio, System Availability, Large Language Models, Caching, Cloudformation, Event Driven Architecture, AI Platforms, Kubernetes, Information Technology, Deployment Automation, Bicep, Free and Open-Source Software, Linkerd (Service Mesh), Machine Learning Operations, TensorRT, Cloud Optimization, Api Gateway, Decoding, Terraform, Dynatrace, Docker - **Published:** August 16, 2026 - **Apply:** https://www.careerjet.com/jobad/usf8c9c00909841e91aeb853c233648a7d ## About the Role * 6+ years of experience in distributed systems, infrastructure, or ML platform engineering. * Strong proficiency in Python and Go, Rust, or C++. * Experience with LLM inference frameworks (vLLM, TensorRT-LLM), Kubernetes, cloud platforms, and GPU optimization., * Bachelor's or Master's degree in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical discipline. * 10+ years of professional experience in distributed systems, infrastructure engineering, cloud platforms, or machine learning platform engineering. * Strong programming skills in Python and at least one systems programming language such as Go, Rust, or C++. * Extensive experience with Large Language Model (LLM) serving, model inference optimization, and production AI infrastructure. * Hands-on experience with vLLM, TensorRT-LLM, Triton Inference Server, Ray Serve, or similar AI serving frameworks. * Strong expertise in Kubernetes, container orchestration, Docker, and cloud-native application architectures. * Experience optimizing GPU workloads using CUDA, NVIDIA GPU technologies, distributed inference, and high-performance AI infrastructure. * Experience with cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform (GCP). * Strong understanding of distributed systems, networking, scalability, observability, and security best practices. * Excellent analytical, communication, collaboration, and technical leadership skills., * Experience designing and operating multi-region AI platforms and globally distributed inference services. * Knowledge of model optimization techniques such as quantization, pruning, compression, speculative decoding, KV cache optimization, and mixed-precision inference. * Experience with MLOps, GitOps, Infrastructure as Code (Terraform, Bicep, CloudFormation), and CI/CD automation. * Familiarity with service mesh technologies such as Istio or Linkerd, API gateways, and event-driven architectures. * Contributions to open-source AI infrastructure projects, technical publications, patents, or conference presentations. * Experience implementing FinOps strategies, cloud cost optimization, and enterprise AI governance. * Experience with multi-region AI deployments and AI infrastructure. * Familiarity with model optimization techniques such as quantization or compression. * Open-source contributions or experience supporting large-scale AI APIs. ## Description We are seeking an AI Platform Engineer to design, build, and operate scalable AI inference platforms for production ML workloads. The ideal candidate will have expertise in distributed systems, LLM serving, GPU optimization, autoscaling, and cloud-native infrastructure, with a strong focus on performance, reliability, and observability., * Design and maintain scalable AI model serving platforms. * Optimize inference performance, GPU utilization, and request routing. * Build autoscaling, deployment, and monitoring solutions. * Implement caching, security, and high-availability strategies. * Collaborate with ML teams to deploy and support production AI models., Bright Vision Technologies is seeking a highly experienced AI Platform Engineer with 10+ years of experience in distributed systems, cloud-native infrastructure, and AI platform engineering to design, build, and operate enterprise-scale AI inference and machine learning platforms. The ideal candidate will possess deep expertise in LLM serving, GPU optimization, Kubernetes, cloud infrastructure, distributed systems, and MLOps, with a proven ability to deliver highly scalable, reliable, secure, and cost-efficient AI platforms supporting production machine learning workloads. Key Responsibilities * Design, build, and maintain scalable AI inference and model-serving platforms for enterprise production environments. * Architect highly available, cloud-native infrastructure supporting Large Language Models (LLMs), foundation models, and machine learning services. * Optimize inference latency, throughput, GPU utilization, memory management, and request scheduling across distributed AI workloads. * Design autoscaling, workload orchestration, traffic management, and intelligent request routing strategies for AI services. * Implement model deployment, versioning, rollback, and lifecycle management using modern MLOps practices. * Develop monitoring, observability, logging, distributed tracing, and alerting solutions to ensure platform reliability and performance. * Implement caching strategies, API gateways, security controls, authentication, authorization, and high-availability architectures. * Collaborate with AI researchers, ML engineers, DevOps teams, and software engineers to deploy and support production AI models. * Drive cloud infrastructure optimization, resource utilization, FinOps initiatives, and operational excellence. * Mentor engineering teams, conduct architecture reviews, and establish best practices for AI platform engineering and cloud-native development. * Evaluate emerging AI infrastructure technologies, model-serving frameworks, and GPU acceleration techniques to drive continuous innovation. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Back(end) to the Future: Embracing the continuous Evolution of Infrastructure and Code](https://www.wearedevelopers.com/videos/440-back-end-to-the-future-embracing-the-continuous-evolution-of-infrastructure-and-code) - [Rate-limiting using eBPF and Istio: How to protect your SaaS customers from themselves](https://www.wearedevelopers.com/videos/100220-rate-limiting-using-ebpf-and-istio-how-to-protect-your-saas-customers-from-themselves) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Get started with securing your cloud-native Java microservices applications](https://www.wearedevelopers.com/videos/123-get-started-with-securing-your-cloud-native-java-microservices-applications) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)