Roles and Responsibilities

• Develop, train, and deploy machine learning models for processing and analyzing IoT sensor and signal data.
• Build predictive models, anomaly detection systems, and real-time decision-making algorithms for connected device ecosystems.
• Investigate and apply state-of-the-art ML/DL techniques for signal understanding, pattern recognition, and intelligent control.
• Design novel approaches for unsupervised and semi-supervised learning in low-label IoT environments.
• Explore and implement federated learning, continual learning, and reinforcement learning to enable adaptive and scalable IoT systems.
• Optimize ML models for edge deployment, considering constraints on compute, latency, and connectivity.
• Collaborate with cross-functional teams to integrate ML pipelines into production IoT platforms.

Required Skills & Qualifications

• Strong proficiency in Python and ML/DL frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Experience with signal processing libraries (e.g., SciPy, librosa, PyWavelets).
• Solid understanding of time-series analysis, feature extraction, and model evaluation for sensor data.
• Familiarity with IoT communication protocols (e.g., MQTT, CoAP) and edge computing architectures.
• Background in deep learning, statistical modeling, and data-driven system design.
• Strong problem-solving, experimentation, and research skills.

Preferred Qualifications

• Experience deploying ML models on edge devices or IoT gateways.
• Knowledge of cloud IoT platforms (AWS IoT, Azure IoT, Google Cloud IoT).
• Experience with real-time streaming frameworks (Kafka, MQTT brokers, etc.).
• Published work or research experience in signal-based ML, anomaly detection, or adaptive systems.

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