Machine Learning Engineer responsible for developing and deploying advanced ML and AI solutions at Zendesk. Collaborating with stakeholders to deliver impactful business outcomes using latest machine learning technologies.
About the role
- Senior AI/ML Engineer at Extreme Networks developing AI-native systems with Generative AI expertise. Building intelligent systems for network design, optimization, security, and support.
Responsibilities
- Design and implement the business logic and modeling that governs agent behavior, including decision-making workflows, tool usage, and interaction policies.
- Develop and refine LLM-driven agents using prompt engineering, retrieval-augmented generation (RAG), fine-tuning, or function calling.
- 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.
- Apply traditional ML modeling techniques (classification, regression, clustering, anomaly detection) to enrich agent capabilities.
- Contribute to the data engineering pipeline that feeds agents, including data extraction, transformation, and semantic chunking.
- Build modular, reusable AI components and integrate them with backend APIs, vector stores, and network telemetry pipelines.
- Collaborate with other AI engineers to create multi-agent workflows, including planning, refinement, execution, and escalation steps.
- Translate GenAI prototypes into production-grade, scalable, and testable services in collaboration with platform and engineering teams.
- Work with frontend developers to design agent experiences and contribute to UX interactions with human-in-the-loop feedback.
- Stay up to date on trends in LLM architectures, agent frameworks, evaluation strategies, and GenAI standards.
Requirements
- Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- 5+ years of experience in ML/AI engineering, including 2+ years working with transformer models or LLM systems.
- Strong knowledge of ML fundamentals, with hands-on experience building and deploying traditional ML models.
- Solid programming skills in Python, with experience integrating AI modules into cloud-native microservices.
- Experience with LLM frameworks (e.g., LangChain, AutoGen, Semantic Kernel, Haystack), and vector databases (e.g., FAISS, Weaviate, Pinecone).
- Familiarity with prompt engineering techniques for system design, memory management, instruction tuning, and tool-use chaining.
- Strong understanding of RAG architectures, including semantic chunking, metadata design, and hybrid retrieval.
- Hands-on experience with data preprocessing, ETL workflows, and embedding generation.
- Proven ability to work with cloud platforms like AWS or Azure for model deployment, data storage, and orchestration.
- Excellent collaboration and communication skills, including cross-functional work with product managers, network engineers, and backend teams.
Benefits
- Health insurance
- Retirement plans
- Flexible work arrangements
- Professional development
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