Job description

Key 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. Qualifications: - 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. Nice to Have: - Experience with LLMOps tools, open-source agent frameworks, or orchestration libraries. - Familiarity with Docker, Docker Compose, and container-based development environments. - Background in enterprise networking, SD-WAN, or network observability tools.Contributions to open-source AI or GenAI libraries.

Requirements