Forward Deployed Engineer, AI Inference (vLLM and Kubernetes)
About The Position
The vLLM and LLM-D Engineering team at Red Hat is looking for a customer obsessed developer to join our team as a Forward Deployed Engineer. In this role, you will not just build software; you will be the bridge between our cutting-edge inference platform (LLM-D, and vLLM) and our customers' most critical production environments. You will interface directly with the engineering teams at our customer to deploy, optimize, and scale distributed Large Language Model (LLM) inference systems. You will solve "last mile" infrastructure challenges that defy off-the-shelf solutions, ensuring that massive models run with low latency and high throughput on complex Kubernetes clusters. This is not a sales engineering role, you will be part of the core vLLM and LLM-D engineering team.
Requirements
- 8+ Years of Engineering Experience in Backend Systems, SRE, or Infrastructure Engineering.
- Deep Kubernetes Expertise: fluent in K8s primitives, from defining custom resources (CRDs, Operators, Controllers) to configuring modern ingress via the Gateway API.
- Deep experience with stateful workloads and high-performance networking, including the ability to tune scheduler logic (affinity/tolerations) for GPU workloads and troubleshoot complex CNI failures.
- AI Inference Proficiency: understand how a LLM forward pass works, KV Caching, prefill/decode disaggregation, context length impacts on performance, and continuous batching in vLLM.
- Proficiency in Python (for model interfaces) and Go (for Kubernetes controllers/scheduler logic).
- Experience with Infrastructure as Code: Helm, Terraform, or similar tools for reproducible deployments.
- Cloud & GPU Hardware Fluency: comfortable spinning up clusters and deploying LLMs on bare-metal and hyperscaler Kubernetes clusters.
Nice To Haves
- Experience contributing to open-source AI infrastructure projects (e.g., KServe, vLLM, Kubernetes).
- Knowledge of Envoy Proxy or Inference Gateway (IGW).
- Familiarity with model optimization techniques like Quantization (AWQ, GPTQ) and Speculative Decoding.
Responsibilities
- Deploy and configure LLM-D and vLLM on Kubernetes clusters.
- Set up and configure advanced deployment like disaggregated serving, KV-cache aware routing, KV Cache offloading etc to maximize hardware utilization.
- Run performance benchmarks, tuning vLLM parameters, and configuring intelligent inference routing policies to meet SLOs for latency and throughput.
- Work directly with customer engineers to write production-quality code (Python/Go/YAML) that integrates our inference engine into their existing Kubernetes ecosystem.
- Debug complex interaction effects between specific model architectures (e.g., MoE, large context windows), hardware accelerators (NVIDIA GPUs, AMD GPUs, TPUs), and Kubernetes networking (Envoy/ISTIO).
- Act as the "Customer Zero" for our core engineering teams, channeling field learnings back to product development, influencing the roadmap for LLM-D and vLLM features.
- Travel only as needed to customers to present, demo, or help execute proof-of-concepts.
Benefits
- Comprehensive medical, dental, and vision coverage
- Flexible Spending Account - healthcare and dependent care
- Health Savings Account - high deductible medical plan
- Retirement 401(k) with employer match
- Paid time off and holidays
- Paid parental leave plans for all new parents
- Leave benefits including disability, paid family medical leave, and paid military leave
- Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
