Ai ML Engineer

Mandatory Skills

• 5+ Years of relevant industry experience
• Strong understanding of machine learning frameworks such as TensorFlow, PyTorch, scikit-learn.
• Experience in designing and executing automated test frameworks for ML/DL systems.
• Familiarity with DevOps tools like Docker, Kubernetes, Jenkins, GitLab CI.
• Exposure to ML model optimization for edge devices (e.g., TensorRT, OpenVINO, Edge TPU).
• Knowledge of MLOps practices, including model versioning and deployment workflows.
• Understanding of natural language processing and Tiny LLMs is a plus.

Key Responsibilities

• Develop and execute test strategies for machine learning, deep learning, and Tiny LLM models running on IoT edge devices.
• Validate model accuracy, robustness, and scalability under real-world IoT data conditions.
• Design automated frameworks to test data pipelines, feature extraction, inference performance, and edge/cloud integration.
• Ensure seamless integration of ML/DL modules into the IoT platform software stack (firmware, middleware, connectivity, and cloud APIs).
• Collaborate with ML developers to ensure models meet production-grade quality standards.
• Work with DevOps engineers to integrate ML model validation into CI/CD workflows.

Requirements:

• Strong understanding of machine learning frameworks such as TensorFlow, PyTorch, scikit-learn.
• Experience in designing and executing automated test frameworks for ML/DL systems.
• Familiarity with DevOps tools like Docker, Kubernetes, Jenkins, GitLab CI.
• Exposure to ML model optimization for edge devices (e.g., TensorRT, OpenVINO, Edge TPU).
• Knowledge of MLOps practices, including model versioning and deployment workflows.
• Understanding of natural language processing and Tiny LLMs is a plus