<div>At Extreme Networks, we create effortless networking experiences that empower people and organizations to advance. As part of our growing AI Competence Center, we are seeking a Senior AI/ML Engineerwith  expertise in Generative AI, multi-agent systems, and LLM-based application development. </div><div>In this role, you will help build the next generation of AI-native systems that combine traditional machine learning, generative models, and autonomous agents. Your work will power intelligent, real-time decisions for network design, optimization, security, and support. </div><h3>Key Responsibilities </h3><ul><li>Design and implement the business logic and modeling that governs agent behavior, including decision-making workflows, tool usage, and interaction policies. </li><li>Develop and refine LLM-driven agents using prompt engineering, retrieval-augmented generation (RAG), fine-tuning, or function calling. </li><li>Understand and model the domain knowledge behind each agent: engage with network engineers, learn the operational context, and encode this understanding into effective agent behavior. </li><li>Apply traditional ML modeling techniques (classification, regression, clustering, anomaly detection) to enrich agent capabilities. </li><li>Contribute to the data engineering pipeline that feeds agents, including data extraction, transformation, and semantic chunking. </li><li>Build modular, reusable AI components and integrate them with backend APIs, vector stores, and network telemetry pipelines. </li><li>Collaborate with other AI engineers to create multi-agent workflows, including planning, refinement, execution, and escalation steps. </li><li>Translate GenAI prototypes into production-grade, scalable, and testable services in collaboration with platform and engineering teams. </li><li>Work with frontend developers to design agent experiences and contribute to UX interactions with human-in-the-loop feedback. </li><li>Stay up to date on trends in LLM architectures, agent frameworks, evaluation strategies, and GenAI standards. </li></ul><h3>Qualifications </h3><ul><li>Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field. </li><li>5+ years of experience in ML/AI engineering, including 2+ years working with transformer models or LLM systems. </li><li>Strong knowledge of ML fundamentals, with hands-on experience building and deploying traditional ML models. </li><li>Solid programming skills in Python, with experience integrating AI modules into cloud-native microservices. </li><li>Experience with LLM frameworks (e.g., LangChain, AutoGen, Semantic Kernel, Haystack), and vector databases (e.g., FAISS, Weaviate, Pinecone). </li><li>Familiarity with prompt engineering techniques for system design, memory management, instruction tuning, and tool-use chaining. </li><li>Strong understanding of RAG architectures, including semantic chunking, metadata design, and hybrid retrieval. </li><li>Hands-on experience with data preprocessing, ETL workflows, and embedding generation. </li><li>Proven ability to work with cloud platforms like AWS or Azure for model deployment, data storage, and orchestration. </li><li>Excellent collaboration and communication skills, including cross-functional work with product managers, network engineers, and backend teams. </li></ul><h3>Nice to Have</h3><ul><li>Experience with LLMOps tools, open-source agent frameworks, or orchestration libraries . </li><li>Familiarity with Docker, Docker Compose, and container-based development environments. </li><li>Background in enterprise networking, SD-WAN, or network observability tools. </li><li>Contributions to open-source AI or GenAI libraries.</li></ul>