About the Job Are you passionate about shaping the future of AI by building infrastructure that ensures large language models and AI agents are safe, reliable, and aligned with human values? Red Hat's OpenShift AI team is seeking a senior ML engineer who combines deep technical expertise with a commitment to responsible AI innovation. As a pivotal contributor to open-source projects like Open Data Hub, KServe, TrustyAI, Kubeflow, and llama-stack, you'll be at the forefront of democratizing trustworthy AI infrastructure. These critical open-source initiatives are transforming how organizations develop, deploy, and monitor machine learning models across hybrid cloud and edge environments. Your work will directly shape the next generation of MLOps platforms, making advanced AI technologies more accessible, secure, and ethically aligned. What you will do: Architect and implement comprehensive safety systems for LLM deployments, including content filtering, output validation, and alignment techniques Design and develop robust guardrailing frameworks that enforce model behavioral boundaries while maintaining performance and user experience Lead the development of monitoring systems to detect and mitigate potential model hallucinations, harmful outputs, and alignment drift in production Build and maintain evaluation frameworks for assessing model safety, including automated testing pipelines for toxicity, bias, and harmful behavior Develop prompt engineering systems and safety layers that ensure reliable and controlled LLM outputs across different use cases and deployment scenarios Implement fine-tuning and RLHF pipelines with a focus on maintaining model alignment and improving safety characteristics Design and deploy systems for LLM output validation, including fact-checking mechanisms and source attribution capabilities Lead technical initiatives around model interpretability and transparency, including debugging tools for understanding model decisions Collaborate with policy and safety teams to translate safety requirements into technical implementations and measurable metrics What you will bring: 5+ years of ML engineering experience, with 3+ years specifically working with transformer-based models and LLMs Deep expertise in prompt engineering, instruction tuning, and RLHF techniques Strong background in implementing AI safety mechanisms and guardrails for production LLM systems Experience with LLM evaluation frameworks and safety metrics Proven track record of building production-grade systems for model monitoring and safety enforcement Strong programming skills in Python and experience with modern LLM frameworks (PyTorch, Transformers, etc.) Experience implementing content filtering and output validation systems The following will be considered a plus: Experience with constitutional AI and alignment techniques Background in implementing RLHF pipelines and fine-tuning large language models Familiarity with LLM deployment platforms and serving infrastructures Experience with model interpretation techniques and debugging tools for LLMs Knowledge of AI safety research and current best practices Understanding of adversarial attacks and defense mechanisms for language models Experience with prompt injection prevention and input sanitization techniques Background in implementing automated testing systems for model safety Advanced degree in Computer Science, ML, or related field with focus on AI safety
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