Machine Learning

1. Conducts research to advance the field of artificial intelligence, develops new algorithms, and publishes findings. 2. Designs builds, and deploys machine learning systems, optimises performance, and integrates models into production environments. 3. Analyses complex data sets, develops predictive models using machine learning techniques, and communicates insights to stakeholders. 4. Develop algorithms and models to understand and generate human language, and apply NLP techniques to real-world problems. 5. Designs and implements computer vision algorithms and systems for tasks like image classification, object detection, and video analysis. 6. Investigate the ethical implications of AI technologies, develop frameworks for responsible AI deployment, and advocate for ethical guidelines. 7. Defines AI product strategy, collaborates with cross-functional teams to develop AI-powered products, and ensures alignment with business goals. 8. Designs scalable and efficient AI systems, selects appropriate hardware and software components, and oversees system integration and deployment. 9. Identifies vulnerabilities in AI systems, develops security measures to protect against attacks, and ensures compliance with security regulations. 10. Advises organisations on AI strategy, identifies opportunities for AI adoption, and provides expertise in implementing AI solutions.

1. collect and analyse data from various sources to extract meaningful insights and inform business decisions. 2. design and create visualisations and dashboards to present data trends and findings to stakeholders. 3. clean, preprocess, and organise data to ensure accuracy and readiness for analysis. 4. perform statistical analysis using tools like Excel and Python to uncover patterns, correlations, and trends in data sets. 5. collaborate with cross-functional teams to understand data requirements and deliver actionable insights. 6. automate data collection and analysis processes to streamline operations and improve efficiency. 7. conduct data quality assessments and implement data cleansing techniques to maintain data integrity. 8. utilise machine learning and predictive modeling techniques to forecast trends and outcomes. 9. stay updated on data analysis techniques and tools to enhance analytical capabilities. 10. communicate findings and recommendations to non-technical stakeholders in a clear and understandable manner.

1. Develop firmware and assist to evaluate, design, build, bench test and debug firmware for new product 2. Works with a variety of interface standards 3. Perform system level design modeling, system integration, algorithm modeling, benchmarking implementation and digital signal processing

1. Lead day-to-day firmware development work and support project from inception to production to meet customer / business qualification 2. Oversees definition, design, verification, testing and documentation for firmware development 3. Provide mentoring to other team members on firmware development

1. Lead the development, upgrades, and integration of standalone and web applications with various systems and data sources 2. Guide junior developers and collaborate with engineers and administrators to support new features and releases 3. Cross-collaborate with other Engineering teams in supporting new features, services and release

1. Design, develop, test, and debug software applications, including low-level core operating system development and validation 2. Collaborate with relevant stakeholders to debug software issues, provide technical training, and offer on-site support for project schedules 3. Work with internal firmware and software teams on platform development and support both internal and external platform bring-up