AI Engineer

Key Responsibilities :End-to-End Model Development: Design, build, validate, and deploy robust machine learning and deep learning models to address business challenges in financial services and manufacturing.Multi-Sector Application: Financial Services (FSI): Develop and implement models for fraud detection, credit risk scoring, algorithmic trading strategies, customer churn prediction, and Anti-Money Laundering (AML) pattern recognition. Manufacturing: Create solutions for predictive maintenance, supply chain optimization, industrial process control, yield prediction, and demand forecasting using time-series and sensor data (). Computer Vision: Build and deploy computer vision models for tasks such as automated optical inspection (AOI), defect detection, object tracking on the factory floor, biometric security, and document analysis.Technical Leadership & Mentorship: Provide technical guidance to junior data scientists, lead project teams, and promote best practices in coding, modeling, and MLOps.Stakeholder Communication: Translate complex technical concepts and model results into clear, actionable business insights for non-technical stakeholders and executive leadership.Data Strategy & Exploration: Work with large, complex datasets from disparate sources. Perform exploratory data analysis to identify key trends, patterns, and opportunities for innovation.Research & Innovation: Stay current with the latest advancements in machine learning, deep learning, and their applications in FSI and manufacturing, and drive the adoption of new technologies and methodologies.Qualifications required Experience:Bachelor Degree in Computer Science, Statistics, Mathematics, Engineering, or another quantitative field.1 years of hands-on experience in a data science or machine learning role, Fresh Graduate can applyProven track record of delivering data science projects in at least two of the following three areas: Financial Services, Manufacturing, and Computer Vision.Strong proficiency in Python and core data science libraries (e.g., pandas, NumPy, scikit-learn, Matplotlib).Hands-on experience with deep learning frameworks such as TensorFlow or PyTorch.Demonstrated experience with computer vision libraries and techniques (e.g., OpenCV, object detection with YOLO/SSD, image segmentation, CNN architectures like ResNet).Expert-level knowledge of SQL for querying relational databases.Experience with cloud computing platforms (AWS, GCP, or Azure) and their associated machine learning services.Educational Background:Bachelor Degree in Computer Science, Statistics, Mathematics, Engineering, or another quantitative field.Specific Skill Needed: • Math and Statistics