AI/ML Technical Lead

Machine Learning Engineer – Technical Lead We are seeking an experienced Machine Learning Engineer – Technical Lead to join our partner's team. In this role, you will lead the design and delivery of advanced machine learning solutions for industrial IoT applications, while mentoring a team of engineers and data scientists to build scalable, production-ready systems. Key Responsibilities: Design, develop, and deploy machine learning models for: predictive maintenance, anomaly detection, asset optimization, and time-series forecasting. Work with large-scale sensor and telemetry data collected from connected devices. Build reliable data pipelines and real-time inference systems integrated across cloud and edge environments. Lead the full lifecycle of ML initiatives, from solution design and experimentation to deployment and optimization. Provide technical leadership and mentorship to ML and software engineering teams, promoting best practices in model development, testing, and deployment. Collaborate closely with product managers, architects, and domain experts to ensure technical solutions align with business objectives. Required Qualifications: Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or a related technical field. 5+ years of hands-on experience in machine learning and software engineering. Demonstrated experience leading technical teams or complex ML projects in production environments. Strong understanding of machine learning and AI concepts, including: supervised and unsupervised learning, classification, regression, clustering, and deep learning techniques. Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Scikit-learn. Strong SQL and cloud platform experience. Hands-on experience working with time-series data. Excellent communication and cross-functional collaboration skills. Preferred Qualifications: Master's or PhD in Computer Science, Electrical Engineering, Statistics or a related field. Experience working in industrial or manufacturing environments. Familiarity with MLOps tools and platforms such as MLflow, Airflow, Docker, and Kubernetes. Experience with signal processing, edge computing or physics-informed machine learning models.