Key Responsibilities
- Design, train, and deploy machine learning and deep learning models for smart city applications.
- Develop AI solutions for computer vision, time-series forecasting, and anomaly detection.
- Build and maintain data pipelines that process data from IoT sensors, traffic cameras, and utility systems.
- Optimize machine learning models for deployment on edge devices with limited computing resources.
- Collaborate with Backend and IoT teams to integrate AI models into production environments.
- Monitor model performance, identify model drift, and retrain models when required.
- Research, evaluate, and implement emerging AI technologies and best practices.
- Document model architecture, assumptions, performance metrics, and technical limitations.
- Ensure model scalability, reliability, and continuous improvement.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 3–6 years of experience in Machine Learning or Artificial Intelligence.
- Strong proficiency in Python programming.
- Hands-on experience with TensorFlow, PyTorch, or Scikit-learn.
- Experience with Computer Vision frameworks such as OpenCV, YOLO, or similar.
- Good understanding of Time-Series Forecasting and Anomaly Detection techniques.
- Experience deploying ML models using Docker, MLflow, TensorFlow Serving, or similar tools.
- Strong knowledge of data structures, algorithms, statistics, and machine learning fundamentals.
Preferred Skills
- Experience with Edge AI frameworks such as TensorRT, ONNX Runtime, or TensorFlow Lite (TFLite).
- Familiarity with GIS data and spatial analytics.
- Experience with cloud-based ML platforms such as AWS SageMaker, Azure Machine Learning, or Google Cloud Vertex AI.
- Exposure to IoT, Smart City, or Public Sector technology projects.
- Knowledge of MLOps concepts and model lifecycle management.
What We Offer
- Opportunity to build AI solutions that improve real-world smart city operations.
- A collaborative, innovative, and fast-paced work environment.
- Competitive salary with performance-based incentives.
- Health insurance and wellness benefits.
- Flexible hybrid working model.
- Learning and development support, including certifications, training programs, and conference sponsorship.
