Software Engineer
Red HatAbout the role
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 MLOps 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 Trustworthy AI and MLOps upstream communities and collaborate closely with the internal cross-functional development teams.
Job Responsibilities:
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
Contribute to developing and integrating model fairness and bias metrics and explainable AI algorithms in OpenShift AI product
Act as an Explainable AI 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
Collaborate with our product management and customer engineering teams to identify and expand product functionalities
Mentor, influence, and coach a team of distributed engineers
Requirements:
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; good English language skills
The following will be considered a plus:
Bachelor's degree in statistics, mathematics, computer science, or a related quantitative field, or equivalent expertise; Master’s or PhD in Machine Learning or NLP 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 Kubernetes and/or OpenShift
Advanced level knowledge and experience in Python, Java, or Go
Familiarity with popular python machine learning libraries such as PyTorch, Tensorflow, Scikit-Learn, and Hugging Face
Pay Transparency
Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual s
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