Quest Global Hiring AI Engineering Intern (Thiruvananthapuram) | Internship

Overview

Quest Global is looking for a motivated AI Engineering Intern to help build an Agentic Edge AI Studio, a multi-agent framework that automates AI model deployment for edge devices. The internship gives hands-on exposure to AI/ML deployment, edge AI optimization, MLOps automation and agent-based systems.

Key Responsibilities

• Study and understand existing AI deployment toolchains and workflows.
• Develop and integrate automated deployment pipelines for edge AI applications.
• Build workflows for model compatibility analysis, validation and performance reporting.
• Assist in implementing AI agent-based mechanisms for automated diagnosis and corrective actions during deployment.
• Support model optimization activities such as pruning, quantization and compilation.
• Validate deployed models using simulators and target hardware platforms.
• Measure and analyse deployment metrics, including accuracy, latency, memory utilisation and deployment effort.
• Document findings, create technical reports and present PoC demonstrations.

Required Skills

• Basic understanding of Machine Learning and Deep Learning concepts.
• Programming experience in Python.
• Familiarity with AI frameworks such as TensorFlow, PyTorch or ONNX.
• Understanding of software development practices and debugging techniques.
• Knowledge of Linux development environments.
• Good analytical and problem-solving skills.

Preferred Skills

• Exposure to Edge AI deployment workflows.
• Knowledge of model optimisation techniques such as quantization and pruning.
• Familiarity with embedded systems, MCUs or edge computing platforms.
• Understanding of AI agents, agentic workflows or MLOps concepts.
• Experience with Git and collaborative software development.

Learning Outcomes

During this internship, the candidate will gain practical experience in:

• Edge AI deployment automation.
• AI model optimisation techniques.
• Agentic AI system development.
• MLOps and deployment workflows.
• Embedded AI validation and benchmarking.
• Technical documentation and PoC development.

Deliverables

By the end of the internship, the intern is expected to contribute to:

• A working automated AI deployment workflow.
• Model compatibility and validation framework.
• Agent-assisted failure diagnosis and recovery mechanism.
• Hardware/simulator benchmarking results.
• Final demonstration, documentation and user guide.