Job Description:

We are seeking an exceptional AI & Machine Learning Engineer to join our team as Technical Lead for Engineering to focus on developing and implementing cutting-edge predictive maintenance solutions based on both physics principles and machine learning techniques. The ideal candidate should have a strong background in machine learning, data collection, and a deep understanding of predictive maintenance techniques, particularly in estimating the Remaining Useful Life (RUL) of assets using both physics-based and data-driven approaches. Furthermore, he/she should have experience of being a Technical Lead for projects while working full time in the industry. Someone with prior experience of independently leading AI/ML projects for predictive maintenance use-cases is desirable.

Required Skills:

Strong knowledge of machine learning concepts, algorithms, and techniques is a necessity.
Proficiency in Python programming language and related libraries (e.g., scikit-learn, TensorFlow, PyTorch).
Experience with data collection, preprocessing, and feature engineering.
Solid understanding of predictive maintenance principles and RUL estimation techniques based on both physics principles and machine learning approaches.
Familiarity with the end-to-end machine learning cycle, including model development, training, validation, and deployment.
Knowledge of MLOps practices and tools for model versioning, monitoring, and deployment.
Experience with cloud technologies (e.g., AWS, Azure, GCP) for machine learning deployments.
Familiarity with data visualization tools (e.g., Matplotlib, Seaborn).
Knowledge of database systems (e.g., SQL, NoSQL) and data storage technologies
Excellent problem-solving, critical thinking and analytical skills will be required.
Strong communication and collaboration abilities.
Organisational abilities to form a technical team and lead it project actualisation.
The ideal candidate would be a go-getter ready to push the envelope, while remaining focused with a results oriented mindset.

Responsibilities:

Be the Technical Lead to design, develop, and deploy machine learning models for predictive maintenance applications, leveraging both physics principles and data-driven techniques.
Organise collection, pre-processing, and analysis of large datasets from various sources to support predictive maintenance initiatives.
Plan, develop and optimize algorithms for predictive maintenance tasks, combining physics-based models and machine learning approaches.
Implement and maintain data pipelines for real-time data ingestion and processing.
Collaborate with cross-functional teams to identify and prioritize predictive maintenance opportunities.
Monitor and evaluate the performance of predictive maintenance models and make necessary improvements.
Implement MLOps best practices to ensure the scalability, reproducibility, and maintainability of machine learning models.
Utilize cloud technologies to deploy, scale, and manage machine learning solutions.
Stay up-to-date with the latest advancements in machine learning, predictive maintenance, and RUL estimation techniques.
Communicate findings and insights to both technical and non-technical stakeholders.

Education:
Master’s degree, or Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, Mechanical Engineering, or a related field with relevant work experience.

Additional Requirements:

Demonstrated work experience through publications, GitHub repositories, or contributions to open-source projects related to machine learning and predictive maintenance
Provide references from previous employers, colleagues, or academic supervisors who can attest to your skills, experience, and work ethics.