Applications Integration

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. Provide ongoing support for applications to ensure they function correctly. 2. Perform routine maintenance, updates, and patches to keep applications up-to-date and secure. 3. Respond to and resolve application-related incidents promptly. Conduct root cause analysis to prevent recurrence of issues and ensure long-term solutions. 4. Monitor application performance and identify areas for improvement. Implement optimisation strategies to enhance application efficiency and user experience. 5. Manage and implement changes to applications in a controlled and systematic manner. Ensure that changes are tested and documented to minimise disruptions. 6. Liaise with software vendors to manage contracts, licenses, and service level agreements (SLAs). 7. Collaborate with stakeholders to understand business requirements and align application services accordingly.

1. Curate, annotate, and refine datasets to train the bot on various intents, entities, and dialogues. 2. Work with natural language processing (NLP) models, refine algorithms, and adjust training parameters to enhance the bot’s performance. 3. Craft conversation flows, responses, and scenarios that are natural, engaging, and aligned with user expectations. 4. Continuously monitor the bot's performance, identify areas for improvement, and implement updates to improve accuracy and user experience. 5. Analyse user interactions and feedback to fine-tune the bot's responses and functionality. 6. Work closely with developers, UX designers, and other stakeholders to integrate the bot into various platforms and ensure it meets business objectives.

1. Monitor networks and systems for security breaches or intrusions and respond promptly to mitigate risks. 2. Conduct vulnerability assessments and penetration testing to identify security weaknesses and recommend remediation strategies. 3. Develop and implement incident response plans to address security breaches, including containment, eradication, and recovery. 4. Conduct security awareness programs and train employees on cybersecurity best practices and policies. 5. analyse security incidents and conduct forensic investigations to determine the root cause and impact of breaches. 6. collaborate with IT teams to implement security controls and configurations based on risk assessments and industry best practices. 7. research and evaluate emerging cybersecurity threats and vulnerabilities to proactively recommend preventive measures. 8. participate in the development and testing of security protocols, encryption techniques, and access control measures. 9. prepare and present reports to management on cybersecurity posture, incidents, and recommendations for improvement.

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. analyse large datasets using statistical methods and machine learning techniques to extract actionable insights. 2. develop predictive models and algorithms to forecast trends, behavior patterns, and business outcomes. 3. create visualisations and reports to communicate findings and recommendations to stakeholders. 4. implement machine learning models and algorithms for tasks such as image recognition, natural language processing, or recommendation systems. 5. collaborate with cross-functional teams to identify opportunities for data-driven solutions and business improvements. 6. clean and preprocess data to prepare it for analysis and modeling. conduct exploratory data analysis to understand patterns, trends, and anomalies in data. 7. evaluate and select appropriate machine learning models and techniques for specific problems. 8. deploy and maintain machine learning models in production environments. 9. stay updated on advancements in machine learning, artificial intelligence, and data science methodologies.