Position: DevOps Engineer LLM & GPU Inference Services
Location: Remote
Duration: 1 Years
Skill Rating
Skills Matrix
Skill
Last Used
Experience In Years/month
Rating (10 points)
1 = newbie 10 = expert
Hands on Exp.
Yes/No
Cloud environments
Large Language Models (LLMs), particularly hosting them to run inference
Distributed Services Experience
GPU (Dedicated Inference Service)
Job Description
We are looking for devs with general cloud services / distributed services experience, with LLM experience as a secondary skill. GPU experience is now low on the list of preferred skills: Dedicated Inference Service
Required Skills-
- Deep experience building services in modern cloud environments on distributed systems (i.e., containerization (Kubernetes, Docker), infrastructure as code, CI/CD pipelines, APIs, authentication and authorization, data storage, deployment, logging, monitoring, alerting, etc.)
- Experience working with Large Language Models (LLMs), particularly hosting them to run inference
- Strong verbal and written communication skills. Your job will involve communicating with local and remote colleagues about technical subjects and writing detailed documentation.
- Experience with building or using benchmarking tools for evaluating LLM inference for various models, engine, and GPU combinations.
- Familiarity with various LLM performance metrics such as prefill throughput, decode throughput, TPOT, and TTFT
- Experience with one or more inference engines: e.g., vLLM, SGLang, and Modular Max
- Familiarity with one or more distributed inference serving frameworks: e.g., llm-d, NVIDIA Dynamo, and Ray Serve etc.
- Experience with AMD and NVIDIA GPUs, using software like CUDA, ROCm, AITER, NCCL, RCCL, etc.
- Knowledge of distributed inference optimization techniques - tensor/data parallelism, KV cache optimizations, smart routing etc.
What You'll Be Working On-
- Develop and maintain an inference platform for serving large language models optimized for the various GPU platforms they will be run on.
- Work on complex AI and cloud engineering projects through the entire product development lifecycle (PDLC) - ideation, product definition, experimentation, prototyping, development, testing, release, and operations.
- Build tooling and observability to monitor system health, and build auto tuning capabilities.
- Build benchmarking frameworks to test model serving performance to guide system and infrastructure tuning efforts.
- Build native cross platform inference support across NVIDIA and AMD GPUs for a variety of model architectures.
- Contribute to open source inference engines to make them perform better on DigitalOcean cloud.
