Machine Learning Engineer designing and optimizing deep learning models for safety - critical environments at Destinus. Shaping the future of high - speed, autonomous flight 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
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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