Research Engineer (AI & Data Science)

• 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.

We’re seeking an experienced 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, and analytics tools that support decision-making and system-level intelligence across multidisciplinary engineering projects., * 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., * 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).

Soft Skills

• Strong analytical and problem-solving mindset.
• Ability to collaborate in cross-functional engineering teams.
• Clear communication and documentation skills.
• Curiosity about how data, models, and system components interact.

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