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.
