Machine Learning Engineer - 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.