About the role

Principal Machine Learning Engineer driving NLP/GenAI tasks and AI application development at Red Hat. Leading implementation of advanced algorithms and building ML pipelines for enterprise solutions.

Responsibilities

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.
Contribute to the design, implementation, and delivery of AI platform capabilities & agentic solutions from concept to production.
Design, build, and evolve ML pipelines that cover data ingestion, preprocessing, feature engineering, training, validation, deployment, and monitoring.
Translate research prototypes and models into production-quality code, ensuring robustness, scalability, and maintainability.
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.
Design, build, and evolve MCP servers and Agents that enable and empower Red Hatters to do business efficiently.
Troubleshoot and resolve production incidents, root-cause errors, data drifts, performance regressions, or infrastructure issues.
Collaborate with cross-functional teams, including finance, operations, sales, and marketing, to understand and meet business needs.
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.

Bachelor’s degree or above in Computer Science, Math, Computational Linguistics, Computer Engineering, or other related fields.
5+ years of professional experience in NLP, with a strong command of Python and frameworks such as Spacy and Hugging Face.
Proven expertise in designing and delivering NLP applications across all stages of the data science lifecycle.
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.
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.
Experience developing highly scalable backend microservices in AWS.
Past experience in building enterprise data platforms that have a high level of governance and compliance requirements.
Comfortable working with a small team in a fast-paced, highly collaborative environment.
Excellent communication, presentation, and writing skills.
Motivated with a passion for quality, learning, and contributing to collective goals.
Deep empathy for your platform's users, leading to a constant focus on removing friction, increasing adoption, and delivering business results.