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# AI Infrastructure Engineer

- **Company:** Netpreme Corporation
- **Location:** Boston, MA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Computer Engineering, Linux, Memory Management, Python (Programming Language), Ansible, AI Infrastructure, Curam Configuration Tools, Graphics Processing Unit (GPU), Retrieval-Augmented Generation, Large Language Models, Caching, Discretization, Containerization, Kubernetes, Information Technology, SGLang, Machine Learning Operations, TensorRT, VLLM, Model Inference, Decoding
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-infrastructure-engineer-boston-ma--5d80cbbc-9676-44ae-9356-4449766762d0

## About the Role

Artificial Intelligence (AI), Optical Networking, Memory Hardware, Memory Management, XLib, Systems Engineering, Benchmarking, Caching, Process Improvement, Computer Engineering, * BS, MS, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.

 * 2+ years of engineering experience in LLM inference, ML systems, GPU systems, or related infrastructure.
 * Hands-on experience deploying and optimizing at least one LLM inference engine (e.g., vLLM, SGLang, or TensorRT-LLM).
 * Solid understanding of LLM inference fundamentals: prefill/decode, batching, KV caching, and latency/throughput tradeoffs.
 * Strong Python and Linux skills, plus hands-on experience with Kubernetes and containerized deployments.
 * Comfortable benchmarking and troubleshooting systems, and communicating clearly in a small engineering team.

Nice to Have

 * Experience with Helm, Ansible, or similar deployment and configuration tools.
 * Experience with distributed inference systems like NVIDIA Dynamo, including parallelism strategies (TP/EP/DP/PP), prefill/decode disaggregation, and KV-cache-aware routing.
 * Experience with advanced inference optimizations such as prefix caching, speculative decoding, and quantization.
 * Experience analyzing LLM architectures and tuning deployments across different hardware platforms to meet SLAs.
 * Contributions to vLLM, SGLang, TensorRT-LLM, or related open-source projects.
 * Experience owning production infrastructure, ideally at an early-stage startup.

## Description

Serving frontier LLMs efficiently at scale is a moving target: every new model and workload reshuffles the tradeoffs across parallelism, batching, KV caching, and scheduling. You'll own Netpreme's inference-serving stack end to end, from tuning vLLM, SGLang, and TensorRT-LLM to architecting distributed serving with NVIDIA Dynamo (prefill/decode disaggregation, KV-cache-aware routing). Working directly with our hardware and systems engineers, you'll build the benchmarks and drive the optimizations that take new models from research to production., * Impact: AI is advancing faster than the infrastructure underneath it. We're working at that layer, unlocking more capability from every GPU while dramatically improving efficiency. The work you do here compounds across state-of-the-art models, systems, and real-world applications.

 * Timing: Joining now means real ownership, including equity, and a direct say in product direction. You'll work from first principles, move quickly from insight to execution, and see your work show up in what we ship, not just in a roadmap.
 * Culture: We care about rigor, clarity, and impact. Disagreement is welcome when it's thoughtful, the best idea wins regardless of who it comes from, and you'll learn something new most weeks.

Your Impact

 * Deploy and operate models in production: Run large language and multimodal models using inference engines like vLLM, SGLang, and TensorRT-LLM.
 * Own the serving infrastructure: Build and maintain distributed inference systems with NVIDIA Dynamo, Kubernetes, Helm, and Ansible, including upgrades, monitoring, troubleshooting, and reliability.
 * Optimize performance: Tune parallelism strategies (TP/EP/DP/PP), batching, KV caching, routing, and scheduling to meet latency, throughput, and SLA targets.
 * Benchmark and measure: Build reproducible benchmarks for latency (TTFT, TPOT, E2E), throughput, concurrency scaling, and system efficiency.
 * Support diverse workloads: Enable tool calling, agentic and coding workflows, RAG, and long-context applications.
 * Bring new tech into production: Evaluate new models, frameworks, and optimizations, and partner with hardware, model, and systems engineers to ship improvements.