> Markdown version of [/jobs/ext/1457549-ml-platform-engineer](https://www.wearedevelopers.com/jobs/ext/1457549-ml-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Platform Engineer - **Company:** Bright Vision Technologies - **Location:** Secaucus, NJ, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Vision, C++ (Programming Language), Data Deduplication, Multiplexing, Distributed Systems, Memory Management, Python (Programming Language), Recommender Systems, Azure Machine Learning, Data Logging, Graphics Processing Unit (GPU), Autoscaling, Delivery Pipeline, Large Language Models, Rate Limiting, Web Filtering, AI Platforms, Kubernetes, Information Technology, Free and Open-Source Software, Machine Learning Operations, TensorRT, Api Gateway - **Published:** July 27, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=28363aa12000430f ## About the Role Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position., * Bachelor's or Master's degree in Computer Science or a related field. * Six or more years of experience in distributed systems, infrastructure, or ML platform engineering. * Strong proficiency in Python and a systems language such as Go, Rust, or C++. * Deep experience operating high-throughput, low-latency services in production. * Hands-on experience with LLM or large model inference frameworks such as vcLLM or TensorRT-LLM. * Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization. * Familiarity with Kubernetes, autoscaling, and modern cloud platforms. * Experience with observability stacks including metrics, tracing, and structured logging. * Solid grounding in performance engineering and capacity planning. * Strong communication and incident response skills., * Open-source contributions to model serving infrastructure. * Experience with multi-region or globally distributed AI serving. * Familiarity with model quantization, distillation, and compression techniques. * Exposure to FinOps for AI workloads and cost-efficient serving design. * Experience supporting external-facing AI APIs at scale. ## Description * Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems. * Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing. * Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints. * Build autoscaling and capacity management systems that balance latency, throughput, and cost. * Tune GPU utilization, memory management, and KV cache strategies for LLM serving workloads. * Integrate model serving with API gateways, identity systems, and observability platforms. * Implement caching, prompt deduplication, and response reuse strategies where appropriate. * Drive end-to-end observability including latency histograms, queue dynamics, GPU utilization, and error tracking. * Develop deployment workflows including canary releases, shadow testing, and automated rollback. * Operate incident response for high-availability AI services and drive durable reliability improvements. * Collaborate with ML and product teams to support new model releases and capability rollouts. * Implement security controls including request signing, content filtering, and abuse detection at the serving layer. * Document operational procedures, performance characteristics, and tuning guidance for internal teams. * Stay current with AI serving research and translate advances into production capabilities. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [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) - [20 billion requests a week: Upgrading Twilio's API gateway at scale](https://www.wearedevelopers.com/videos/100234-20-billion-requests-a-week-upgrading-twilio-s-api-gateway-at-scale) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)