לוח משרות דרושים NLP/Machine Learning עבודה היברידית

The OpenShift AI team is looking for a Machine Learning Engineer with experience in building, scaling, and monitoring AI/ML systems to join our rapidly growing engineering team. Our focus is to create a platform, partner ecosystem, and community by which enterprise customers can solve problems to accelerate business success using AI. This is a very exciting opportunity to shape the deployment and reliability of GenAI workloads, contribute to the development of the RHOAI product, participate in open source communities, and be at the forefront of the exciting evolution of AI. Youll join an ecosystem that fosters continuous learning, career growth, and professional development.
As a core ML engineer for one of our OpenShift AI teams, you will have the opportunity to design and build systems that monitor, validate, and improve AI model performance in production. You will work as part of an evolving development team to rapidly design, secure, build, test, and release new capabilities. The role is primarily an individual contributor who collaborates closely with other ML engineers, software developers, and cross-functional teams. You should have a passion for observability, MLOps, and building robust systems for real-world AI.
Our commitment to open source innovation extends beyond our products - its embedded in how we work and grow. we embrace change especially in our fast-moving technological landscape and have a strong growth mindset. That's why we encourage our teams to proactively, thoughtfully, and ethically use AI to simplify their workflows, cut complexity, and boost efficiency. This empowers our associates to focus on higher-impact work, creating smart, more innovative solutions that solve our customers' most pressing challenges.
What you will do:
Design and build observability and assistance tools to help customers optimize large-scale AI initiatives running on Kubernetes
Innovate in the MLOps and AI observability and deployment optimization domains by contributing to upstream communities
Collaborate with product, engineering, and research teams to improve model trust and performance
Write unit and integration tests and work with quality engineers to ensure product quality
Use CI/CD best practices to deliver solutions into RHOAI as part of our productization efforts
Proactively utilize AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) for code generation, auto-completion, and intelligent suggestions to accelerate development cycles and enhance code quality.
Contribute to a culture of continuous improvement by sharing technical knowledge and insights
Communicate effectively with stakeholders and team members to ensure visibility of ML performance
Represent RHOAI in external engagements including open source communities and customer meetings
Mentor and guide junior engineers and contribute to team growth
Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI.

Required Principal Machine Learning Engineer GenAI Benchmarking & Validation Infrastructure
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 our 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.