Machine Learning Engineer

Job Summary:

We are on a mission to bring the power of open-source LLMs and vLLM to every enterprise on the planet. As an ML Engineer, you will work closely with our product and research teams to develop SOTA deep learning software. You will collaborate with our technical and research teams to develop training and deployment pipelines, implement model compression algorithms, and productize deep learning research. If you are someone who wants to contribute to solving challenging technical problems at the forefront of deep learning, this is the role for you. Join us in shaping the future of AI!

What You Will Do:

• Build & Deploy Safely on OpenShift AI: Design, develop, containerize, and deploy AI-powered features and models using Red Hat OpenShift AI components (e.g., Workbenches, Model Registry, Pipelines), ensuring alignment with responsible AI principles and safety requirements from the outset.

• Integrate & Vet AI Services: Leverage and critically evaluate open-source models (e.g., Granite), integrating them safely, securely, and responsibly within applications running on OpenShift. Assess models for potential biases and safety risks before integration.

• Master AI Safety & OpenShift Toolkits: Utilize AI engineering frameworks, vector databases, and specific tools within OpenShift AI for AI safety, explainability, bias detection, development, serving, monitoring, and automation of the AI system lifecycle.

• Design & Implement Guardrails: Design, implement, and test robust safety guardrails for AI interactions, including input validation, prompt safety techniques, output content filtering, and oversight mechanisms for RAG patterns. Automate these checks using OpenShift Pipelines and GitOps practices where applicable.

• Manage Models with MLOps & Safety: Utilize (and sometimes contribute to building) models and leverage OpenShift AI Serving technologies to deploy, manage versions, and continuously monitor for performance, safety metrics, bias, and drift, ensuring the reliability and ethical operation of AI models in production.

• Evaluate Holistically: Focus on product-specific evaluation of AI models and approaches, rigorously assessing their performance, safety, fairness, bias, robustness, cost, and adherence to ethical guidelines within the OpenShift environment.

• Stay Current on Safety & AI: Keep pace with advancements in the general AI landscape (models, techniques), especially emerging threats, mitigation techniques, AI safety research, and relevant regulatory landscapes, alongside OpenShift AI platform features.

• Collaborate for Trust: Work closely with product managers, application developers, platform engineers, data scientists, and relevant governance/ethics/security teams to integrate AI capabilities smoothly, ensuring systems are trustworthy, compliant, and adhere to enterprise standards.

What You Will Bring:

• A Safety-Conscious Builder with Platform Sense: You have a strong software engineering background (Python proficiency is key, Java is a plus), understand containerization (Docker, Podman), Kubernetes/OpenShift fundamentals, and prioritize building secure and safe systems.

• AI Practitioner Aware of Risks: You actively experiment with Foundation Models (LLMs, etc.), understand how to apply them practically, and are keenly aware of their potential risks, limitations, and ethical considerations.

• OpenShift AI Familiar: You have hands-on experience with Red Hat OpenShift AI / OpenShift Data Science and its core components (Model Serving, Pipelines, Workbenches). Understanding of MLOps concepts is essential.

• Tool-Savvy with a Safety Lens: You’re familiar with the AI engineering stack (LLama Stack, LangChain, Vector DBs, etc.) and are eager to apply or learn tools/techniques specifically for AI safety, explainability, and bias mitigation within an OpenShift context.

• Pragmatic & Responsible: You focus on building useful, reliable AI features, with a strong commitment to responsible AI development, deployment, and minimizing potential harm.

• Fast Learner: You thrive in the fast-paced AI field and are comfortable continuously learning new platform features, AI techniques, and safety best practices.

• Not Necessarily an ML PhD: Your strength lies in applying, deploying, and s