Staff Software Engineer , Anywhere Cloud - AI Systems & Runtimes

Cloudera•Austin, TX

17d•Hybrid

About The Position

Ready to take cloud innovation to the next level? Join Cloudera’s Anywhere Cloud team and help deliver a true “build your own pipeline, bring your own engine” experience — enabling data and AI workloads to run anywhere, without friction or vendor lock-in. This isn’t just cloud management — it’s about building a consistent, secure, and compliant cloud experience that gives organizations full access to all their data, anywhere. With the acquisition of Taikun, we’re simplifying Kubernetes and cloud management even further, creating a unified, scalable, future-ready platform. If you’re passionate about Kubernetes — not just using it, but building it at the core, managing workloads across hybrid clouds and data centers, and obsessing over performance and DevOps — this is where you belong. We are seeking a Staff Software Engineer to lead the architecture and delivery of our cloud‑native AI platform. In this high‑impact role, you will bridge the gap between cutting‑edge AI research and production‑grade Kubernetes environments. You will build the “nervous system” of our AI stack—optimizing how we run and manage open‑source models (Llama, Qwen, etc.) using K8s‑native patterns like Custom Resources (CRDs) and Operators, enabling agentic AI to thrive, and designing integration patterns that let our product teams and customers consume AI capabilities seamlessly.

Requirements

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.

Nice To Haves

Model Fine-Tuning: Experience with efficient fine-tuning techniques (PEFT, LoRA/QLoRA) on custom datasets.
GPU Optimization: Familiarity with CUDA programming or profiling GPU performance (Nsight systems).
Open Source: Contributions to open-source AI projects (HuggingFace transformers, vLLM, etc.).

Responsibilities

Enterprise AI Services: Design and implement elegant, scalable application services (Go/Node.js) that wrap AI capabilities for enterprise use.
K8s-Native AI Orchestration: Lead the deployment of inference servers (vLLM, Triton) using KServe, KubeRay, or Knative to ensure serverless-style scaling for AI workloads.
Developer Velocity: Build internal tooling, SDKs, and "AI Gateways" that enhance team agility and simplify the integration of Foundation Models (Llama, GPT) into product features.
RAG & Prompt Engineering: Architect robust Retrieval-Augmented Generation (RAG) pipelines and prompt management services that integrate seamlessly with vector databases and enterprise data sources.
Cross-Functional Collaboration: Partner with UI engineers, UX designers, and Product Management to ensure the AI platform is not just powerful, but highly usable for internal developers.
Infrastructure & Security: Ensure AI workloads are secure, multi-tenant, and optimized for GPU resource scheduling (MIG, fractional GPUs) within Kubernetes.