Come join a team of industry and science leaders to achieve a vision of empowering innovation through state-of-the-art artificial intelligence and machine learning.

aiXplain is looking for a Contract Senior AI/ML Engineer who would be instrumental in creating personalized protection strategies for motor applications. Your expertise in time-series analysis, signal processing, machine learning, deep learning, reinforcement learning, and optimization will drive the development of tailored protection policies, ensuring optimal circuit breaker performance and minimizing disruptions. This will require strong problem-solving and communication skills for multidisciplinary teamwork.

This is a 3-6 month remote contract opportunity.

Responsibilities

• Innovate protection strategies: Analyze signals / time-series for differentiation between inrush and short-circuit events.
• Efficient data pipelines: Design scalable data pipelines for preprocessing, feature extraction, and model training/validation.
• Model optimization: Fine-tune machine learning models to achieve project goals and define validation metrics for optimal performance.
• Simulation integration: Integrate protection policies into simulations, analyze outcomes, and refine models for desired objectives.
• Real-world implementation: Collaborate with engineers to apply protection policies in practical scenarios, simulate outcomes, and measure performance.
• Latency optimization: Analyze computation time and classification performance to optimize feature extraction and decision-making processes.

Qualifications

• Education: At least a Master’s degree in Electrical Engineering, Computer Science, Mechatronics, or related fields with a focus on AI/ML.
• Expertise: Extensive experience in time-series analysis, signal processing, machine learning, and deep learning techniques.
• Programming: Strong Python programming skills, proficient in libraries like TensorFlow 2.0, PyTorch, LightGBM, XGBoost, TSFresh, etc.
• Decision systems: Previous experience with gradient-boosted decision trees and rule-based systems for complex applications.
• Backend proficiency: Ability to create backend software using SQL/NoSQL databases and API frameworks (Django, Flask, FastAPI).
• Cloud platforms: Proficiency in cloud platforms (AWS, Azure & GCP) and experiment tracking tools (MLflow, W&B, NNI, Ray-Tune, Syne-Tune, etc.).
• MLOps and Big-data: Proficiency in Platforms such as GitHub, Hadoop, PySpark, H2O, Kubernetes, Docker, Kubeflow, KServe, Ray, DeepSpeed, etc.
• AutoML and model efficiency: Interest in neural architecture search and inference optimization via model compression and distillation methods.
• Real-world solutions: Track record of deploying large-scale and state-of-the-art AI/ML solutions for practical applications in various industries.