Senior ML Engineer, AI Infrastructure

The Senior Machine Learning Engineer – GenAI
is responsible for designing, implementing, and operating large-scale systems and tools for AI model benchmarking, optimization, and validation. Unlike a traditional ML Engineer focused primarily on model training, this role centers on
building the infrastructure, automation, and services that enable systematic evaluation and performance tuning of LLMs at scale
.

This position combines
deep understanding of model serving frameworks, GPU optimization, and benchmarking methodologies
with strong software engineering skills to deliver reliable, reproducible, and production-grade evaluation pipelines. The engineer will design and maintain
validation-as-a-service platforms
that allow internal and external stakeholders to assess models across latency, throughput, accuracy, and cost dimensions—integrating seamlessly with Red Hat's AI ecosystem and industry-standard GenAI tooling.

A core aspect of this role is
creating a robust, extensible benchmarking and validation framework
capable of running across diverse inference engines, hardware configurations, and deployment environments, while providing actionable insights for model selection, optimization, and integration.

What You Will Do

• Benchmarking Platform Development: Design and implement scalable benchmarking pipelines for LLM performance measurement (latency, throughput, accuracy, cost) across multiple serving backends and hardware types.
• Optimization Tooling: Build utilities and automation to profile, debug, and optimize inference performance (GPU utilization, memory footprint, CUDA kernels, parallelism strategies).
• Validation-as-a-Service: Develop APIs and self-service platforms for model evaluation, enabling teams to run standardized benchmarks on demand.
• Serving Integration: Integrate and operate high-performance serving frameworks (vLLM, TGI, LMDeploy, Triton) with cloud-native deployment patterns.
• Dataset & Scenario Management: Create reproducible workflows for dataset preparation, augmentation, and scenario-based testing to ensure robust evaluation coverage.
• Observability & Diagnostics: Implement real-time monitoring, logging, and metrics dashboards (Prometheus, Grafana) for benchmark and inference performance.
• Cloud-Native Orchestration: Deploy and manage benchmarking workloads on Kubernetes (Helm, Argo CD, Argo Workflows) across AWS/GCP GPU clusters.
• Integration with GenAI Tooling: Leverage Hugging Face Hub, OpenAI SDK, LangChain, LlamaIndex, and internal frameworks for streamlined evaluation workflows.
• Performance Engineering: Identify bottlenecks, apply targeted optimizations, and document best practices for inference scalability.
• Ecosystem Leadership: Track emerging frameworks, benchmarks, and optimization techniques to continuously improve the evaluation platform.

What You Ill Bring

• Advanced Python for backend development, data processing, and ML/GenAI pipelines.
• Kubernetes (Deployments, Services, Ingress) and Helm for large-scale distributed training and inference workloads.
• LLM training, fine-tuning, and optimization (PyTorch, DeepSpeed, HF Transformers, LoRA/PEFT).
• GPU optimization expertise: CUDA, mixed precision, tensor/sequence parallelism, memory management, and throughput tuning.
• High-performance model serving with vLLM, TGI, LMDeploy, Triton, and API-based serving (OpenAI, Mistral, Anthropic).
• Benchmarking and evaluation pipelines: dataset preparation, accuracy/latency/throughput measurement, and cost–performance tradeoffs.
• Multi-model, multi-engine comparative testing for optimal deployment decisions.
• Hugging Face Hub for model/dataset management, including private hosting and pipeline integration.
• GenAI development tools: OpenAI SDK, LangChain, LlamaIndex, Cursor, Copilot.
• Argo CD & Argo Workflows for reproducible, automated ML pipelines.
• CI/CD (GitHub Actions, Jenkins) for ML lifecycle automation.
• Cloud (AWS/GCP) for provisioning, running, and optimizing GPU workloads (A100, H100, etc.).
• Monitoring & observability (Prometheus, Grafana) and databases (PostgreSQL, SQLAlchemy).

Nice To Have

• Distributed training across multi-node, multi-GPU clusters.
• Advanced model evaluation: bias/fairness, robustness, and domain-specific benchmarks.
• Experience with OpenShift/RHOAI for enterprise AI deployments.
• Benchmarking frameworks: GuideLLM, HELM, Eval Harness.
• Security scanning for artifacts/containers (Trivy, Grype).
• Tradeoff-analysis tooling for model selection and deployment

About Red Hat
Red Hat is the world's leading provider of enterprise 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
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