Senior MLOps Engineer - Responsible AI

About the job

Do you want to be part of a team that is focused on scaling the deployment, trustworthy AI, and monitoring Foundation Models and Large Language Models (LLMs)? The OpenShift AI team is looking for a Senior Software Engineer with Kubernetes and MLOps experience to join our rapidly growing engineering team. Our team’s focus is to make machine learning model deployment and monitoring seamless, scalable, and trustworthy across the hybrid cloud and the edge. This is a very exciting opportunity to build and impact the next generation of hybrid cloud MLOps platforms. 

In this role, you'll be contributing as a technical expert for explainable AI and fairness for the responsible AI features of the open source Open Data Hub project by actively participating in KServe, TrustyAI, Kubeflow,  and several other open source communities. You will work as part of an evolving development team to rapidly design, secure, build, test and release model serving, trustworthy AI, and model registry capabilities. The role is primarily an individual contributor who will be a key notable contributor to MLOps upstream communities and collaborate closely with the internal cross-functional development teams. 

What you will do

• Be an influencer and leader in MLOps and Explainable AI, Fairness & Bias related open source communities to help build an active MLOps open source ecosystem for Open Data Hub and OpenShift AI

• Act as a MLOps SME within Red Hat by supporting customer facing discussions, presenting at technical conferences, and evangelizing OpenShift AI within the internal community of practices

• Research and design new features for open source MLOps communities such as KServe and TrustyAI

• Provide technical vision and leadership on critical and high impact projects 

• Mentor, influence, and coach a team of distributed engineers

What you will bring

• Strong research and development experience in Explainable Artificial Intelligence (XAI) with a focus on Large Language Models (LLMs), model-agnostic interpretability methods, bias detection and mitigation, and metrics for assessing fairness, transparency, and interpretability in the complex AI models.

• Recent hands on experience in deploying and maintaining machine learning models in production environments with respect to explainable AI 

• Technical leadership acumen

• Passion for writing and maintaining reliable code

• Hands on experience in Kubernetes

• Comfortable working in a distributed remote team environment

• Excellent written and verbal communication skills; fluent English language skills

The following will be considered a plus: 

• Bachelor's degree in statistics, mathematics, computer science, operations research, or a related quantitative field, or equivalent expertise; Master’s or PhD is a big plus.

• Experience in engineering, consulting or another field related to model serving and monitoring, model registry, explainable AI, deep neural networks, in a customer environment or supporting a data science team

• Highly experienced in OpenShift

• Advanced level knowledge and experience in Python, Go, or Rust

• Familiarity with popular python machine learning libraries such as PyTorch, Tensorflow, Scikit-Learn, and Hugging Face

#LI-REMOTE

#LI-AM4

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 have the flexibility to choose the work environment that suits their needs from in-office to fully remote to office-flex. 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. Opportunities are open. Join us.

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