Digital and Directorate AI Developer

Key Responsibilities:

1.Support data ingestion, transformation, and quality checks to produce reliable, ML-ready datasets.

2. Develop and deploy machine learning models for business use cases (e.g., claims risk, lapse prediction, fraud detection).

3. Convert machine learning models into APIs and support their integration into business systems.

4. Monitor the performance of deployed models and support recalibration when needed.

5. Contribute to the development of dashboards (Power BI or similar) that visualize AI/ML outputs for business stakeholders.

6. Support Finance, Actuarial, Risk, and Product teams with predictive analytics tools and outputs.

7. Assist in validating model outputs and insights before release to business stakeholders.

8. Build automation for recurring reporting and manual workflows.

9. Apply data security, privacy, and ethical AI standards across all development work.

10. Support Risk & Compliance initiatives related to data protection.

11. Maintain technical documentation of AI/ML solutions for audit and regulatory purposes.

12. Collaborate with data stewards and data scientists on data preparation and experimentation for AI projects.

 
Knowledge, experience and qualifications required
 

Knowledge, Experience and Qualifications required and Essential Competencies - External

1. University degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related field with strong emphasis on mathematical and programming proficiency.

2. 2–4 years’ experience developing and deploying AI/machine learning solutions.

3. Experience applying machine learning algorithms and statistical techniques: regression, simulation, scenario analysis, modelling, clustering, decision trees, etc.

4. Ability to identify, analyze, and resolve complex technical issues, ensuring optimal performance, scalability, and user experience.

5. Dedication to writing clean, maintainable, and well-documented code with a focus on application quality, performance, and security.

6. Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers.

7. Working knowledge of machine learning frameworks/libraries and Microsoft Fabric.

Technical / Functional Competencies

1. Machine learning and statistical modelling techniques.

2. Programming proficiency in Python and SQL; familiarity with API development.

3. Knowledge on langGraph and LangChain will be an added advantage

4. Working knowledge of Microsoft Fabric or similar cloud/data platforms.

5. Understanding of MLOps practices for deploying and monitoring models in production.

6. Database management systems.

7. General awareness of the insurance industry context is an advantage.

Key Responsibilities:

1.Support data ingestion, transformation, and quality checks to produce reliable, ML-ready datasets.

2. Develop and deploy machine learning models for business use cases (e.g., claims risk, lapse prediction, fraud detection).

3. Convert machine learning models into APIs and support their integration into business systems.

4. Monitor the performance of deployed models and support recalibration when needed.

5. Contribute to the development of dashboards (Power BI or similar) that visualize AI/ML outputs for business stakeholders.

6. Support Finance, Actuarial, Risk, and Product teams with predictive analytics tools and outputs.

7. Assist in validating model outputs and insights before release to business stakeholders.

8. Build automation for recurring reporting and manual workflows.

9. Apply data security, privacy, and ethical AI standards across all development work.

10. Support Risk & Compliance initiatives related to data protection.

11. Maintain technical documentation of AI/ML solutions for audit and regulatory purposes.

12. Collaborate with data stewards and data scientists on data preparation and experimentation for AI projects.

 
Knowledge, experience and qualifications required
 

Knowledge, Experience and Qualifications required and Essential Competencies - External

1. University degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related field with strong emphasis on mathematical and programming proficiency.

2. 2–4 years’ experience developing and deploying AI/machine learning solutions.

3. Experience applying machine learning algorithms and statistical techniques: regression, simulation, scenario analysis, modelling, clustering, decision trees, etc.

4. Ability to identify, analyze, and resolve complex technical issues, ensuring optimal performance, scalability, and user experience.

5. Dedication to writing clean, maintainable, and well-documented code with a focus on application quality, performance, and security.

6. Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers.

7. Working knowledge of machine learning frameworks/libraries and Microsoft Fabric.

Technical / Functional Competencies

1. Machine learning and statistical modelling techniques.

2. Programming proficiency in Python and SQL; familiarity with API development.

3. Knowledge on langGraph and LangChain will be an added advantage

4. Working knowledge of Microsoft Fabric or similar cloud/data platforms.

5. Understanding of MLOps practices for deploying and monitoring models in production.

6. Database management systems.

7. General awareness of the insurance industry context is an advantage.