Cloud AI Engineer

Analog Devices is hiring for the role of Cloud AI Engineer! Responsibilities: Design, develop, and deploy Agentic AI systems that can autonomously perform tasks, make decisions, and interact with users and other systems Build and optimize cloud-based infrastructure on AWS, Azure, or GCP to support AI/ML workloads and applications Implement and maintain CI/CD pipelines for automated deployment of AI models and cloud services Contribute to operational improvements by identifying bottlenecks, optimizing resource utilization, and implementing monitoring and alerting systems Collaborate with cross-functional teams to integrate AI capabilities into existing products and services Develop and maintain technical documentation for AI systems, cloud architectures, and operational procedures Participate in code reviews, design discussions, and knowledge-sharing sessions with team members Monitor and troubleshoot AI model performance, cloud infrastructure issues, and system reliability Stay current with emerging trends in AI, machine learning, and cloud technologies through continuous learning Assist in implementing security best practices and compliance requirements for cloud and AI systems Work on proof-of-concept projects to evaluate new AI frameworks, tools, and cloud services Support the team in scaling AI solutions from development to production environments Requirements: Education: Bachelor's or Master's degree in Computer Science, Engineering, or related technical field (2024-2026 graduates preferred) Agentic AI Experience: Demonstrated projects or coursework in Agentic AI, autonomous systems, LLM-based agents, or AI orchestration frameworks Cloud Platform Knowledge: Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) through projects, internships, or certifications AI/ML Coursework: Completed courses in Artificial Intelligence, Machine Learning, Deep Learning, or Natural Language Processing Programming Skills: Proficiency in Python and familiarity with AI/ML libraries (TensorFlow, PyTorch, scikit-learn, LangChain, etc.) Communication Skills: Strong written and verbal communication skills with the ability to explain complex technical concepts clearly Documentation: Demonstrated ability to create clear, comprehensive technical documentation Team Collaboration: Proven ability to work effectively in team environments, collaborate across functions, and contribute to shared goals Preferred Qualifications Experience with containerization (Docker) and orchestration (Kubernetes) Familiarity with Infrastructure as Code (Terraform, CloudFormation, ARM templates) Knowledge of DevOps practices and tools (Git, Jenkins, GitLab CI/CD, GitHub Actions) Experience with Large Language Models (LLMs) and prompt engineering Understanding of MLOps practices and model deployment strategies Cloud certifications (AWS Certified Cloud Practitioner, Azure Fundamentals, Google Cloud Associate, etc.) Contributions to open-source projects or published research in AI/ML Experience with vector databases and retrieval-augmented generation (RAG) systems