**The Principal Machine Learning Engineer – GenAI** is responsible for **hands-on design, development, and operation** of large-scale systems and tools for AI model benchmarking, optimization, and validation. Unlike traditional ML Engineers focused mainly on training models, this role centers on **building, running, and continuously improving the infrastructure, automation, and services** that enable rigorous, repeatable, and production-grade model evaluation at scale. This is a **hands-on principal role** that combines strategic technical leadership with active engineering execution. You will own the architecture, implementation, and optimization of benchmarking and validation capabilities across Red Hat’s AI ecosystem. This includes architecting **Validation-as-a-Service platforms** , delivering high-performance benchmarking pipelines, integrating with leading GenAI frameworks, and setting industry standards for model evaluation quality and reproducibility. The role demands **deep GenAI domain expertise, architectural foresight, and direct coding involvement** to ensure evaluation platforms are flexible, extensible, and optimized for real-world, large-scale use. **What you will do** + **Architect and lead** scalable benchmarking pipelines for LLM performance measurement (latency, throughput, accuracy, cost) across multiple serving backends and hardware types. + **Build optimization & profiling tools** for inference performance, including GPU utilization, memory footprint, CUDA kernel efficiency, and parallelism strategies. + **Develop Validation-as-a-Service platforms** with APIs and self-service tools for standardized, on-demand model evaluation. + **Integrate and optimize model serving frameworks** (vLLM, TGI, LMDeploy, Triton) and API-based serving (OpenAI, Mistral, Anthropic) in production environments. + **Establish dataset & scenario management workflows** for reproducible, comprehensive evaluation coverage. + **Implement observability & diagnostics systems** (Prometheus, Grafana) for real-time benchmark and inference performance tracking. + **Deploy and manage workloads in Kubernetes** (Helm, Argo CD, Argo Workflows) across AWS/GCP GPU clusters. + **Lead performance engineering efforts** to identify bottlenecks, apply optimizations, and document best practices. + **Stay ahead of the GenAI ecosystem** by tracking emerging frameworks, benchmarks, and optimization techniques, and integrating them into the platform. **What you will bring** + Advanced Python for ML/GenAI pipelines, backend development, and data processing. + Kubernetes (Deployments, Services, Ingress) with Helm for large-scale distributed workloads. + Deep expertise in LLM serving frameworks (vLLM, TGI, LMDeploy, Triton) and API-based serving (OpenAI, Mistral, Anthropic). + GPU optimization mastery: CUDA, mixed precision, tensor/sequence parallelism, memory optimization, kernel-level profiling. + Design and operation of benchmarking/evaluation pipelines with metrics for accuracy, latency, throughput, cost, and robustness. + Experience with Hugging Face Hub for model/dataset management and integration. + Familiarity with GenAI tools: OpenAI SDK, LangChain, LlamaIndex, Cursor, Copilot. + Argo CD and Argo Workflows for reproducible ML orchestration. + CI/CD (GitHub Actions, Jenkins) for ML workflows. + Cloud expertise (AWS/GCP) for provisioning, running, and optimizing GPU workloads (A100, H100, etc.). + Monitoring and observability (Prometheus, Grafana) and database experience (PostgreSQL, SQLAlchemy). **Nice to Have** + Distributed training across multi-node, multi-GPU environments. + Advanced model evaluation: bias/fairness testing, robustness analysis, domain-specific benchmarks. + Experience with OpenShift/RHOAI for enterprise AI workloads. + Benchmarking frameworks: GuideLLM, **HELM (Holistic Evaluation of Language Models)** , Eval Harness. + Security scanning for ML artifacts and containers (Trivy, Grype). + Design of tradeoff-analysis tools for model selection and deployment. \#AIHiring \#LI-YB1 **About Red Hat** Red Hat (https://www.redhat.com/) is the world’s leading provider of enterprise open source (https://www.redhat.com/en/about/open-source) software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact. **Inclusion at Red Hat** Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village. **Equal Opportunity Policy (EEO)** Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law. **Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. 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