Senior Machine Learning Engineer

As a member of the Data And AI team at Red Hat, you will be instrumental in leading digital-first execution and transformation by strategically leveraging data & AI for customers, partners, and associates. Your primary focus will be on building and delivering strategic AI agents & MCP Servers on the Data & AI platform, designed to enhance human capabilities and accelerate business workflows across the enterprise. **Key Responsibilities:** - Contribute to the design, implementation, and delivery of AI platform capabilities from concept to production - Implement advanced algorithms and tools for Agentic AI tasks - Integrate Large Language Models (LLMs) into enterprise applications - Develop Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured data sources - Design and develop agentic AI platform architectures enabling autonomous reasoning, planning, and task execution - Build AI agents and multi-agent workflows using frameworks such as AutoGen, LangGraph, WatsonX, or similar orchestration systems - Develop enterprise AI co-pilots for operational workflows, document automation, and decision support systems - Build AI Data Pipelines covering data ingestion, preprocessing, feature engineering, training, validation, deployment, and monitoring - Integrate enterprise data lakes, APIs, and knowledge bases into agent workflows - Ensure enterprise-grade performance, scalability, and security - Implement evaluation, guardrails, hallucination mitigation, and safety mechanisms - Design, build, and evolve MCP servers and Agents for efficient business operations - Troubleshoot and resolve production incidents, root-cause errors, data drifts, performance regressions, or infrastructure issues - Collaborate with cross-functional teams to understand and meet business needs - Collaborate closely with software engineers, data engineers, and SRE/DevOps to integrate ML services into broader systems **Qualifications Required:** - Bachelors degree or above in Computer Science, Math, Computational Linguistics, Computer Engineering, or related fields - 3+ years of professional experience in NLP/Generative/Agentic AI, Python, and ML frameworks (PyTorch, TensorFlow, or similar) - Hands-on experience with LLM-based systems and generative AI applications - Experience designing production ML systems at scale - Strong understanding of distributed systems and cloud-native architectures - 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 - Experience with LangGraph, LangChain, AutoGen, WatsonX, and/or Python/Java-based AI libraries - Experience developing highly scalable backend microservices - Past experience in building enterprise AI platforms with high governance and compliance requirements - Motivated with a passion for quality, learning, and contributing to collective goals - Deep empathy for platform users, focusing on removing friction, increasing adoption, and delivering business results **About Red Hat:** Red Hat is the worlds leading provider of enterprise open source software solutions, delivering high-performing Linux, cloud, container, and Kubernetes technologies. With a focus on transparency, collaboration, and inclusion, Red Hat offers a flexible work environment across 40+ countries. Red Hat values creativity, passion, and diversity in contributing to open source innovation. If you need assistance completing the online job application at Red Hat, please email application-assistance@redhat.com. As a member of the Data And AI team at Red Hat, you will be instrumental in leading digital-first execution and transformation by strategically leveraging data & AI for customers, partners, and associates. Your primary focus will be on building and delivering strategic AI agents & MCP Servers on the Data & AI platform, designed to enhance human capabilities and accelerate business workflows across the enterprise. **Key Responsibilities:** - Contribute to the design, implementation, and delivery of AI platform capabilities from concept to production - Implement advanced algorithms and tools for Agentic AI tasks - Integrate Large Language Models (LLMs) into enterprise applications - Develop Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured data sources - Design and develop agentic AI platform architectures enabling autonomous reasoning, planning, and task execution - Build AI agents and multi-agent workflows using frameworks such as AutoGen, LangGraph, WatsonX, or similar orchestration systems - Develop enterprise AI co-pilots for operational workflows, document automation, and decision support systems - Build AI Data Pipelines covering data ingestion, preprocessing, feature engineering, training, validation, deployment, and monitoring - Integrate enterprise data lakes, APIs, and knowledge bases into agent wor