Principal Machine Learning Engineer-AgenticAI, GenAi/LLM, , MCP, Python/Golang

About the Job : The Data And AI team is a  highly focused effort to lead digital-first execution and transformation at Red Hat leveraging data & AI strategically for our customers, partners, and associates. The engineering team is focused on building and delivering strategic AI agents & MCP Servers built on our Data & AI platform, designed to augment human capabilities, accelerate business workflows, and scale operations across the enterprise. In this role,  you'll take ownership of end-to-end ML systems, champion best practices, and deliver impactful, production-grade models. You’ll work autonomously, mentor others, and collaborate with data, engineering, and product teams to bring AI & agentic capabilities into production. What You Will Do? GenAI Development: Lead the research and implementation of advanced algorithms and tools for NLP/GenAI tasks. Drive the development of next-generation AI/ML applications in a highly collaborative environment. Solution Delivery: Contribute to the design, implementation, and delivery of AI platform capabilities & agentic solutions from concept to production. Build ML/NLP Pipelines: Design, build, and evolve ML pipelines that cover data ingestion, preprocessing, feature engineering, training, validation, deployment, and monitoring. Ensure the successful training and evaluation of NLP models, refining them based on statistical analysis. Productionize Models: Translate research prototypes and models into production-quality code, ensuring robustness, scalability, and maintainability. Model Evaluation & Tuning: Select appropriate algorithms and modeling techniques, perform hyperparameter tuning, and conduct comparative experimentation. Evaluate and validate model performance using advanced metrics (e.g., ROC-AUC, precision/recall curves, calibration, fairness, drift) and set up continuous validation/regression checks. Build & Operate Systems: Design, build, and evolve MCP servers and Agents that enable and empower Red Hatters to do business efficiently. Instrument models and systems with monitoring, logging, alerting, and automated healing or scaling mechanisms. Troubleshoot & Support: Troubleshoot and resolve production incidents, root-cause errors, data drifts, performance regressions, or infrastructure issues. Collaborate & Mentor: Collaborate with cross-functional teams, including finance, operations, sales, and marketing, to understand and meet business needs. You will also collaborate closely with software engineers, data engineers, and SRE/DevOps to integrate ML services into broader systems. Set Standards: Mentor more junior engineers, lead code reviews, and help establish ML lifecycle and quality standards. Stay current with emerging ML research, frameworks, and tooling, and proactively propose improvements or experiments. What You Will Bring? Education & Experience: Bachelor’s degree or above in Computer Science, Math, Computational Linguistics, Computer Engineering, or other related fields. NLP Expertise: 5 years of professional experience in NLP, with a strong command of Python and frameworks such as Spacy and Hugging Face. ML Lifecycle Mastery: Proven expertise in designing and delivering NLP applications across all stages of the data science lifecycle. GenAI & Frameworks: Deep understanding of machine learning frameworks and experience in Generative AI application development. This includes working knowledge of TensorFlow, TensorFlow Serving, Keras, and PyTorch, as well as experience with LLMs, Embedding models, and Vector Databases. Software Engineering Excellence: Exceptional software engineering skills that lead to an elegant and maintainable data platform. Proficiency in at least one general-purpose programming language (e.g., Python, Go, Java, Rust, etc.). Experience with LangGraph, LangChain, Autogen and/or Python/Java-based AI libraries for GenAI applications Scalable Systems: Experience developing highly scalable backend microservices in AWS. Enterprise ... (truncated, view full listing at source)