We are seeking a skilled Machine Learning Engineer to support the design, development, deployment, and operationalisation of machine learning and AI solutions within a banking environment.
The role will focus on building scalable, secure, and production-ready ML solutions that support business decision-making, automation, risk management, customer insights, and digital innovation.

What you'll do:

• Design, build, test, deploy, and maintain machine learning models and AI-driven solutions.
• Work with data scientists, data engineers, software engineers, architects, and business stakeholders.
• Translate business problems into practical ML/AI solutions.
• Develop ML pipelines for training, testing, deployment, monitoring, and retraining.
• Build APIs or services to expose ML models to business applications.
• Perform feature engineering, data preparation, experimentation, and model evaluation.
• Support MLOps practices including model versioning, monitoring, CI/CD, and automation.
• Monitor model performance, data quality, model drift, and production behaviour.
• Ensure solutions are scalable, secure, maintainable, and aligned to governance standards.
• Document model logic, technical designs, deployment processes, and support procedures.

Your Expertise:

• 3+ years’ experience as a Machine Learning Engineer, AI Engineer, Data Scientist, MLOps Engineer, or similar.
• Strong hands-on experience with Python (essential) and SQL.
• Experience with Scala, R, Java, or C++ would be advantageous.
• Experience developing and deploying ML models into production or enterprise environments.
• Strong understanding of machine learning algorithms, statistical modelling, feature engineering, and model evaluation.
• Experience with libraries/frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Pandas, NumPy, or similar.
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Experience with MLOps concepts such as model deployment, monitoring, versioning, experiment tracking, retraining pipelines, and CI/CD.
• Experience with MLflow, Docker, Kubernetes, Airflow, Databricks, SageMaker, Azure ML, or Vertex AI would be advantageous.
• Banking, fintech, risk, fraud, payments, or customer analytics experience would be advantageous.

Qualifications:

• Relevant qualification in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, AI, or a related field.
• Relevant cloud, AI, ML, or data certifications would be advantageous.

Technical Skills

• Python (essential)
• SQL
• Scala, R, Java, or C++
• Machine Learning / AI
• Feature Engineering
• Model Deployment
• Model Monitoring
• MLOps
• Scikit-learn, TensorFlow, PyTorch
• Pandas, NumPy
• MLflow
• Docker / Kubernetes
• Git / CI/CD
• REST APIs
• Spark / PySpark / Databricks
• AWS / Azure / GCP

Core Competencies

• Strong analytical and problem-solving ability.
• Strong coding and engineering mindset.
• Ability to move models from prototype to production.
• Good communication and stakeholder engagement skills.
• Comfortable working in cross-functional teams.
• Proactive, detail-oriented, and solution-focused.

Nice-to-Have

• Generative AI / LLM experience.
• RAG, vector databases, embeddings.
• LangChain, OpenAI, Azure OpenAI, Amazon Bedrock, or similar.
• Model explainability and governance.
• Experience in regulated banking or financial services environments.