Job Title: Senior AI Engineer-Responsible AI
Job Type: Permanent
Job Location: Waterford,Ireland
Company Overview:
Our client is the world's leading provider of enterprise open-source software solutions, utilizing a community-driven approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. With a presence in over 40 countries, our associates have the flexibility to choose a work environment that best suits their needs, ranging from in-office to fully remote options.
The Role:
In this role, you will serve as a technical expert in the areas of explainable AI and fairness, focusing on the responsible AI features of the open-source Open Data Hub project. Your primary responsibilities will involve active participation in key open-source communities, including KServe, TrustyAI, Kubeflow, and others.
You will work as an integral member of a dynamic development team, contributing to the rapid design, security, development, testing, and deployment of model-serving capabilities, trustworthy AI solutions, and model registry functionalities.
Job Responsibilities:
- Serve as a thought leader and influencer in the MLOps, LLM Guardrails, Explainable AI, Fairness, and Bias domains, contributing to the development of a vibrant open-source ecosystem for Open Data Hub and OpenShift AI.
- Contribute to the design and integration of model fairness and bias metrics, as well as explainable AI algorithms, within the OpenShift AI product suite.
- Act as a Subject Matter Expert (SME) in Explainable AI, providing expertise in customer-facing discussions, delivering presentations at technical conferences, and advocating for OpenShift AI within internal communities of practice.
- Conduct research and design innovative features for open-source MLOps communities, including KServe and TrustyAI, to enhance their capabilities and impact.
- Collaborate closely with product management and customer engineering teams to identify opportunities for expanding and refining product functionalities.
- Mentor, guide, and influence a distributed team of engineers, fostering collaboration and promoting professional development within the team.
Requirements:
- Extensive experience in research and development within the field of Explainable Artificial Intelligence (XAI), with a particular focus on Guardrails for Large Language Models (LLMs), model-agnostic interpretability methods, bias detection and mitigation, as well as metrics for evaluating fairness, transparency, and interpretability in complex AI models.
- Recent, hands-on experience in deploying and maintaining machine learning models in production environments, specifically related to Explainable AI.
- Demonstrated technical leadership capabilities, with a proven track record of guiding and influencing teams and projects.
- A strong commitment to writing and maintaining reliable, high-quality code.
- Practical experience with Kubernetes, including hands-on deployment and management of containerized applications.
- Comfortable working effectively within a distributed, remote team environment.
- Exceptional written and verbal communication skills, with proficiency in the English language.
Qualification:
- A Bachelor's degree in Statistics, Mathematics, Computer Science, or a related quantitative field, or equivalent professional expertise. A Master's or PhD in Machine Learning or Natural Language Processing (NLP) is highly desirable.
- Demonstrated experience in engineering, consulting, or a related domain involving model serving and monitoring, model registry, explainable AI, deep neural networks, preferably within a customer-facing environment or supporting a data science team.
- Extensive hands-on experience with Kubernetes and/or OpenShift.
- Advanced proficiency and experience with programming languages such as Python, Java, or Go.
- Familiarity with widely used Python machine learning libraries, including but not limited to PyTorch, TensorFlow, Scikit-Learn, and Hugging Face.
If you are interested in this role or would like to discuss it further, please contact Nidhi at +353 1 645 5244 or email [email protected].
