AI/ML Engineer

We are seeking skilled AI/ML Engineers to design, build, and deploy intelligent data-driven solutions across key financial domains including Payments, Investor Services & Trade, Pricing & Billing, and Liquidity Management. The role focuses on leveraging advanced machine learning techniques to optimise processes, enhance decision-making, and deliver scalable, production-grade analytics solutions.

Experience Levels

• Mid-Level AI/ML Engineer: 4–7 years of relevant experience
• Senior AI/ML Engineer: 7+ years of relevant experience

Key Responsibilities

• Design, develop, and deploy machine learning models and AI solutions aligned to business use cases.
• Work across financial domains including Payments, Trade & Investor Services, Pricing & Billing, and Liquidity Management.
• Build and maintain scalable data pipelines and feature engineering workflows.
• Translate complex business problems into analytical and ML solutions.
• Collaborate with data engineers, analysts, and business stakeholders to deliver end-to-end solutions.
• Ensure model performance, monitoring, and continuous improvement in production environments.
• Apply best practices in MLOps, model governance, and data quality.
• Support real-time and batch processing use cases where applicable.

Senior Level Additional Responsibilities:

• Lead solution design and architecture for AI/ML initiatives.
• Mentor junior team members and provide technical guidance.
• Drive strategic AI adoption across business domains.
• Engage with senior stakeholders and influence decision-making.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Engineering, or related field.

Technical Skills

• Strong programming skills in Python (mandatory).
• Experience with ML frameworks (e.g., Tensor Flow, Py Torch, Scikit-learn).
• Proficiency in SQL and working with large datasets.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Knowledge of data engineering concepts (ETL pipelines, data lakes, streaming).
• Familiarity with MLOps tools (e.g., MLflow, Docker, Kubernetes, CI/CD pipelines).
• Experience with data visualisation tools (e.g., Power BI, Tableau).

Domain Experience (Highly Preferred)

• Pricing & Billing: Dynamic pricing models, revenue optimisation, billing analytics.
• Liquidity Management: Cash flow forecasting, liquidity risk modelling, treasury analytics.

Core Competencies

• Strong analytical and problem-solving skills.
• Ability to work with complex financial datasets.
• Excellent communication and stakeholder engagement skills.
• Attention to detail and commitment to data quality.
• Ability to manage multiple priorities in a fast-paced environment.