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We are seeking a Junior AI & Data Science Engineer with a strong foundation in Python and a growing interest in machine learning, data processing, and intelligent system design. In addition to hands‑on model development and data work, you will contribute to early‑stage system definition, requirements analysis, and integration planning. You will help build AI models, data pipelines, analytics tools that support decision‑making and system‑level intelligence across multidisciplinary engineering projects.
Job Responsibility
Develop ML models for prediction, classification, anomaly detection, and decision support
Build and maintain data pipelines for structured, unstructured, and sensor-derived datasets
Implement Python modules for model training, evaluation, and deployment workflows
Create simple UI tools using Python frameworks for visualising data, model outputs, and system status
Support data engineering tasks including feature extraction, dataset curation, and data quality checks
Participate in model optimisation, benchmarking, validation, and documentation
Work with senior engineers to elicit, clarify, and document AI-related requirements
Research COTS AI tools, cloud services, and data-processing technologies for potential integration
Contribute to system architecture by defining data flows, module interactions, and integration points
Support integration testing and system-level debugging across data, software, and hardware boundaries
Requirements
Python proficiency with experience in NumPy, Pandas, and scikit-learn
Understanding of ML fundamentals and interest in model development and evaluation
Understanding of data pipelines, analytics workflows, and intelligent systems
Ability to interpret technical requirements and translate them into actionable tasks
Interest in system-level thinking and multidisciplinary engineering
Nice to have
Familiarity with deep learning using TensorFlow or PyTorch
Knowledge of data visualisation and dashboarding tools
Experience with system diagrams and interface documentation
Exposure to MLOps, CI/CD, or model deployment pipelines
Exposure to cloud ML platforms such as Azure ML, AWS Sagemaker, or GCP Vertex AI
Experience with Python UI frameworks (Tkinter, PySide, PyQt)