AI & Machine Learning, Analyst

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About the Role

This role is ideal for a fresh graduate who is eager to build practical experience in AI engineering, data engineering, MLOps, cloud technologies, and enterprise data platforms within a highly regulated financial services environment.

It offers an opportunity to work on enterprise-scale AI, machine learning, and Generative or/and Agentic AI initiatives that support customer engagement, distribution, underwriting, claims, operations, and risk management across the organization.

Working alongside data architects, machine learning engineers, data scientists, cloud engineers, and business stakeholders, you will contribute to the design, development, deployment, and operationalization of AI solutions on modern cloud-based platforms.

1. AI & Machine Learning Development

• Design, develop, test, and deploy machine learning and Generative AI solutions to solve business challenges across customer, distribution, claims, underwriting, and operations domains.

• Develop and optimize NLP, LLM, recommendation, classification, forecasting, and predictive analytics models.

• Support model experimentation, evaluation, fine-tuning, and performance monitoring.

• Implement Retrieval Augmented Generation (RAG) solutions leveraging enterprise data sources and vector databases.

• Assist in prompt engineering and enhancement of Generative AI applications to improve response quality, security, and business relevance.

2. AI Platform & MLOps Engineering

• Develop and maintain machine learning pipelines throughout the model lifecycle, including training, validation, deployment, monitoring, and retraining.

• Support CI/CD implementation for AI and machine learning applications.

• Contribute to model versioning, experiment tracking, and reproducible AI development practices.

• Monitor model performance, drift, accuracy, and operational metrics.

3. Data Engineering & Architecture

• Build and maintain data ingestion, transformation, and feature engineering pipelines.

• Work with structured and unstructured datasets from enterprise platforms, data lakes, and operational systems.

• Support data quality, lineage, metadata management, and governance initiatives.

• Collaborate with enterprise architects to ensure AI solutions align with data architecture standards and technology roadmaps.

4. Cloud & Application Integration

• Develop APIs and microservices that expose AI capabilities for business applications.

• Integrate AI services with enterprise platforms, digital channels, and customer-facing applications.

• Leverage cloud-native services on Azure to develop scalable AI solutions.

• Support containerization and deployment using technologies such as Docker and Kubernetes.

5. Responsible AI & Governance

• Adhere to enterprise AI governance standards, security policies, and data privacy requirements.

• Support implementation of responsible AI practices, including model explainability, fairness, transparency, and risk controls.

• Ensure AI solutions comply with financial services regulatory and compliance requirements.

6. Collaboration & Innovation

• Work closely with business stakeholders to understand requirements and translate them into technical solutions.

• Participate in agile project delivery and contribute to product backlogs and sprint activities.

• Research emerging AI technologies and evaluate their applicability to insurance use cases.

• Present findings, prototypes, and technical recommendations to team members and stakeholders.

• A bachelor's degree in computer science, Artificial Intelligence, Data Science, or related fields.

• Relevant certification in cloud platforms and services (AWS, Google Cloud, Azure) for deploying AI solutions.

• Fresh graduates are encouraged to apply

• Publications or contributions to the AI community through research papers, open-source projects, GitHub portfolio, or conferences.

• Exposure in utilising frameworks like Langchain, Llamaindex, and Crew AI to create intelligent and autonomous agents.

• Strong knowledge of machine learning, deep learning, and generative models, with experience in training and fine-tuning GenAI models.

• Exposure in machine learning operations (MLOps) and familiarity with mainstream AI services (e.g., OpenAI, Google AI) and hands-on exposure with API integration.

• Proficiency in Python, with experience using AI frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.

• Practical exposure in at least one area, such as NLP, computer vision, speech recognition, or recommendation algorithms.

• Exposure in leveraging and fine-tuning large language models (LLMs) and Small Language Models to optimize AI applications for specific business needs.

• Familiarity with natural language processing techniques (tokenization, NER, sentiment analysis).

• Experience with computer vision tasks (image classification, object detection, segmentation).

• Ability to work with large datasets and knowledge of data preprocessing techniques (handling missing data, data augmentation, normalization).

• Strong analytical and problem-solving abilities with excellent communication skills and the ability to collaborate effectively with cross-functional teams.

• Strong teamwork and collaboration mindset with the ability to translate business problems into data and AI solutions.