> Markdown version of [/jobs/ext/2682113-staff-ai-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/2682113-staff-ai-infrastructure-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). --- # Staff AI Infrastructure Engineer - **Company:** SEEKR TECHNOLOGIES INC. - **Location:** Reston, VA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Cloud Computing, Cloud Engineering, Cloud Storage, Computer Networks, Continuous Delivery, Continuous Integration, Distributed Systems, Python (Programming Language), Machine Learning, Enterprise Messaging Systems, Octopus Deploy, Oracle (Applications), Performance Tuning, Software Architecture, Systems Development Life Cycle, Prometheus, Software Engineering, Systems Architecture, AI Infrastructure, Scripting, Graphics Processing Unit (GPU), Google Cloud, Cloud Platform System, Spring Cloud, Large Language Models, Grafana, Multi-Agent Systems, Caching, Event Driven Architecture, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Hardware Acceleration, Machine Learning Operations, TensorRT, Hardware Infrastructure, Oracle Cloud Infrastructure, Docker, Programming Languages - **Published:** August 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/staff-ai-infrastructure-engineer-reston-va--f4f63f61-8a41-4a4a-91aa-9373dae1ee21 ## About the Role * 8-12+ years of professional software engineering experience building distributed systems, cloud infrastructure, or large-scale platform services * Architects systems, drives technical direction cross-functionally * 4 year or higher degree or additional relevant experience, in addition to years of work experience * Demonstrated success designing and operating production Kubernetes environments supporting cloud-native applications and distributed services. * Strong software engineering skills using Python and one or more modern programming languages such as Go, Rust, or C++. * Proven ability to design, build, and operate production AI or machine learning infrastructure. * Expertise developing and optimizing large-scale AI inference platforms, including GPU utilization, distributed inference, batching, caching, quantization, and accelerator performance. * Familiarity with modern AI serving technologies such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, Ray Serve, or similar platforms. * Knowledge of distributed computing, networking, storage systems, cloud-native architectures, and infrastructure automation using technologies such as Kubernetes, Helm, Argo CD, Docker, Prometheus, Grafana, OpenTelemetry, and Infrastructure-as-Code tools. * Experience developing enterprise AI platforms, autonomous agents, or multi-agent systems, including orchestration, tool execution, governance, observability, and evaluation. * Familiarity with event-driven architectures, distributed messaging systems, and public cloud platforms including AWS, Azure, Oracle Cloud Infrastructure, or Google Cloud Platform. * Demonstrated technical leadership, including driving architectural decisions, mentoring engineers, and leading complex technical initiatives across cross-functional teams. * Demonstrated ability to analyze, profile, and optimize AI systems for performance, scalability, reliability, and cost across distributed compute environments., Amazon Web Services (AWS), Analysis Skills, Architectural Services, Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Automation, Best Practices, C++ Programming Language, Caching, Cloud Applications, Cloud Architecture, Cloud Computing, Cloud Storage, Computer Networks, Computer Systems, Continuous Deployment/Delivery, Continuous Integration, Cross-Functional, Data Quality, Distributed Applications, Distributed Computing, Docker, Emerging Technology, Engineering, Finance, GPU (Graphics Processing Unit), Government, Identify Issues, Incident Response, Machine Learning, Memory Hardware, Mentoring, Messaging Technology, Microsoft Windows Azure, Operational Improvement, Oracle, Performance Management, Performance Tuning/Optimization, Problem Solving Skills, Production Machining, Production Support, Production Systems, Programming Languages, Public Cloud, Python Programming/Scripting Language, Rust Programming Language, Scalable System Development, Scientific Research, Software Development, Software Engineering, System Architecture, Systems Maintenance, Team Player, Technical Leadership, Technical/Engineering Design ## Description Seekr is building the infrastructure that powers the next generation of enterprise AI. As a Staff AI Infrastructure Engineer, you will design, build, and operate the platforms that enable large-scale training, serving, evaluation, and deployment of foundation models and autonomous AI agents. You will work across distributed systems, Kubernetes, GPU infrastructure, high-performance inference, and enterprise AI platforms to build secure, scalable, and highly reliable systems capable of serving workloads ranging from edge AI deployments to trillion-parameter foundation models. This role requires deep expertise in distributed systems, cloud-native infrastructure, AI platform engineering, and production software development. You will collaborate with research scientists, software engineers, product teams, and infrastructure engineers to define the architecture and technical direction of Seekr's AI platform., * Design, develop, deploy, and maintain production AI infrastructure supporting model training, fine-tuning, inference, evaluation, and agentic AI workloads. * Design and operate scalable Kubernetes-based infrastructure supporting GPU-accelerated workloads across cloud, on-premises, hybrid, and edge environments. * Architect and optimize high-performance inference platforms capable of serving models ranging from resource-constrained edge deployments to trillion-parameter foundation models, with a focus on latency, throughput, scalability, reliability, and cost efficiency. * Build and maintain distributed systems that enable reliable scheduling, orchestration, deployment, monitoring, and lifecycle management of AI workloads. * Develop enterprise platforms supporting autonomous and multi-agent AI systems, including secure tool execution, orchestration, memory, evaluation, governance, and observability. * Design, implement, and automate AI infrastructure using Infrastructure-as-Code, GitOps, CI/CD pipelines, and modern software engineering practices. * Evaluate and integrate emerging AI infrastructure technologies, model serving frameworks, hardware accelerators, and cloud-native platforms to improve platform performance, scalability, and reliability. * Collaborate with engineering, research, product, and cross-functional teams to deliver secure, scalable, and production-ready AI platforms. * Lead technical design discussions, perform architecture reviews, mentor engineers, and establish engineering standards and best practices across the AI Infrastructure organization. * Participate in production support activities, including troubleshooting complex distributed systems, performance tuning, incident response, and continuous operational improvement. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [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 build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Reference Architecture of AI in the Cloud](https://www.wearedevelopers.com/videos/1613-reference-architecture-of-ai-in-the-cloud) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [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) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)