Forward Deployed Engineer, AI Inference (vLLM and Kubernetes)

Red Hat is the world’s leading provider of enterprise open source software solutions, and they are seeking a Forward Deployed Engineer to join their vLLM and LLM-D Engineering team. This role involves deploying, optimizing, and scaling distributed Large Language Model (LLM) inference systems while working closely with customer engineers to integrate solutions into their existing infrastructure.

Responsibilities:

• Orchestrate Distributed Inference: Deploy and configure LLM-D and vLLM on Kubernetes clusters. You will set up and configure advanced deployment like disaggregated serving, KV-cache aware routing, KV Cache offloading etc to maximize hardware utilization
• Optimize for Production: Go beyond standard deployments by running performance benchmarks, tuning vLLM parameters, and configuring intelligent inference routing policies to meet SLOs for latency and throughput. You care about Time Per Output Token (TPOT), GPU utilization, GPU networking optimizations, and Kubernetes scheduler efficiency
• Code Side-by-Side: Work directly with customer engineers to write production-quality code (Python/Go/YAML) that integrates our inference engine into their existing Kubernetes ecosystem
• Solve the "Unsolvable": 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)
• Feedback Loop: Act as the "Customer Zero" for our core engineering teams. You will channel 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

Requirements:

• 8+ Years of Engineering Experience: You have a decade-long track record in Backend Systems, SRE, or Infrastructure Engineering
• Customer Fluency: You speak both 'Systems Engineering' and 'Business Value'
• Bias for Action: You prefer rapid prototyping and iteration over theoretical perfection. You are comfortable operating in ambiguity and taking ownership of the outcome
• Deep Kubernetes Expertise: You are fluent in K8s primitives, from defining custom resources (CRDs, Operators, Controllers) to configuring modern ingress via the Gateway API. You have 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: You understand how a LLM forward pass works. You know what KV Caching is, why prefill/decode disaggregation matters, why context length impacts performance, and how continuous batching works in vLLM
• Systems Programming: Proficiency in Python (for model interfaces) and Go (for Kubernetes controllers/scheduler logic)
• Infrastructure as Code: Experience with Helm, Terraform, or similar tools for reproducible deployments
• Cloud & GPU Hardware Fluency: You are comfortable spinning up clusters and deploying LLMs on bare-metal and hyperscaler Kubernetes clusters
• 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