Design and implement elegant, scalable application services (Go/Node.js) that wrap AI capabilities for enterprise use.
Lead the deployment of inference servers (vLLM, Triton) using KServe, KubeRay, or Knative to ensure serverless-style scaling for AI workloads.
Build internal tooling, SDKs, and "AI Gateways" that enhance team agility and simplify the integration of Foundation Models (Llama, GPT) into product features.
Architect robust Retrieval-Augmented Generation (RAG) pipelines and prompt management services that integrate seamlessly with vector databases and enterprise data sources.
Partner with UI engineers, UX designers, and Product Management to ensure the AI platform is not just powerful, but highly usable for internal developers.
Ensure AI workloads are secure, multi-tenant, and optimized for GPU resource scheduling (MIG, fractional GPUs) within Kubernetes.

Bachelor’s degree with 6+ years of software engineering experience (or equivalent Masters/PhD tenure), with at least 2+ years focused on AI/ML systems.
Expert proficiency in Python (for AI ecosystem) and strong competence in a systems language like Go or Rust/C++ (for high-performance serving layers).
Deep understanding of LLM deployment challenges and runtimes (e.g., vLLM, ONNX, TorchServe, Triton).
Familiarity with quantization techniques (AWQ, GPTQ) to optimize model size/speed.
Experience building complex workflows using tools like LangChain or LlamaIndex, and deploying them on containerized infrastructure (Docker/Kubernetes).
Ability to navigate the rapidly changing AI landscape, filtering hype from practical engineering solutions, and driving technical alignment across teams.

Staff Software Engineer – AI Systems, Runtimes

Key skills