Job Overview
Role: Cloud AI Engineer Location: Bangalore Experience: Freshers / Entry-Level Qualification: Bachelor's or Master's degree in Computer Science, Engineering, or related technical field Key Skills: Python, AI/ML Frameworks, Agentic AI, Cloud Platforms (AWS, Azure, GCP), Docker, Kubernetes, Terraform, DevOps
Job Description
Analog Devices is looking for an innovative and driven Cloud AI Engineer to join its development center in Bangalore, India. This entry-level role is uniquely designed for recent engineering graduates eager to build cutting-edge Agentic AI platforms, optimize cloud architectures, and deploy intelligent models at scale. You will join an elite engineering ecosystem working on Agentic Systems Design, Cloud Optimization, and Pipeline Automation for machine learning models.
Roles and Responsibilities
- Agentic Systems Design: Build, orchestrate, and maintain autonomous Agentic AI components capable of executing logic steps, interacting with backend applications, and making contextual decisions.
- Cloud Optimization: Create and refine scalable cloud-native infrastructure environments across AWS, Azure, or GCP to handle distributed AI/ML data training workloads.
- Pipeline Automation: Design and run streamlined CI/CD frameworks for the automated deployment of trained machine learning models into microservices.
- System Monitoring & Troubleshooting: Identify operational bottlenecks, track live AI model inference degradation, and setup comprehensive observability dashboards.
- Proof of Concepts: Run independent proof-of-concept tasks to check, bench-test, and recommend emerging AI engines, open-source model configurations, and managed cloud offerings.
Skills and Eligibility Criteria
Educational Background: Must have completed or be completing a Bachelor's or Master's degree in Computer Science Engineering, Information Technology, Electronics & Communication, or a closely allied technical discipline.
Experience: Freshers / Entry-Level.
Mandatory Technical Skills:
- High degree of proficiency in Python programming, along with academic exposure to AI/ML framework libraries such as TensorFlow, PyTorch, scikit-learn, or LangChain.
- Demonstrated hands-on projects, personal repositories, or advanced coursework centered around Agentic AI, autonomous AI frameworks, LLM-based agents, or AI orchestration.
- Basic experience or foundational certifications across at least one primary cloud platform environment (such as AWS, Azure, or Google Cloud Platform).
- Solid grasp of foundational concepts in Artificial Intelligence, Deep Learning, Machine Learning models, and Natural Language Processing (NLP).
- Proven capability to draft clean, highly comprehensive technical documentation, systems diagrams, and architectural procedures.
Competencies:
- Familiarity with application containerization tools (Docker) and orchestration layers (Kubernetes).
- Understanding of Infrastructure as Code frameworks such as Terraform, AWS CloudFormation, or ARM templates.
- Exposure to core DevOps pipelines (Git, Jenkins, GitHub Actions, or GitLab CI/CD).
- Familiarity with vector databases, semantic search techniques, and Retrieval-Augmented Generation (RAG) system workflows.
- Possession of fundamental cloud provider credentials (such as AWS Certified Cloud Practitioner or Azure Fundamentals).