Why This Role Stands Out
This hybrid role offers unparalleled growth potential within a globally renowned financial institution, allowing you to build and deploy cutting-edge AI services and shape the future of wholesale banking. If you're a skilled Machine Learning Scientist eager to productionize innovative solutions and drive impact across international markets, this is an exciting opportunity to advance your career. You'll thrive by collaborating with diverse teams and developing robust MLOps pipelines, so don't miss out on applying!
Quick Overview
Job Description
Our client is a leading international bank.
If you're looking for a career that will help you stand out, join our client, and fulfil your potential - whether you want a career that could take you to the top, or an exciting new direction, we offer opportunities, support and rewards that will take you further.
We're one of the largest banking and financial services organisations in the world, with a network that covers more than 50 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people fulfil their hopes and realise their ambitions.
We are seeking a Senior Machine Learning Scientist
You'll play a key part in designing, building, deploying, and operating production-grade AI services (ML and/or GenAI) that are secure, scalable, and reusable across Wholesale use cases
In this role you'll:
- Design, build, deploy, and operate production-grade AI services (ML and/or GenAI) that are secure, scalable, and reusable across Wholesale use cases
- Productionise PoC/PoV work into hardened solutions with clear non-functional requirements (performance, resilience, cost, security) and defined service ownership
- Build and maintain MLOps/LLMOps pipelines (CI/CD, automated testing, packaging, promotion/rollback, model/version management) to enable repeatable releases
- Develop reusable engineering assets (libraries, templates, reference architectures, infrastructure-as-code patterns) to reduce technical debt and accelerate delivery • Implement observability for AI services (logging/metrics/tracing), model performance monitoring, and quality/drift checks with actionable alerting
- Partner with data scientists, data engineers, platform teams, and governance/risk stakeholders to ensure end-to-end delivery meets control, auditability, and documentation expectations
- Translate business requirements into technical designs; communicate trade-offs and recommendations clearly to both technical and non-technical stakeholders
- Contribute to engineering standards and ways of working (code reviews, design reviews, documentation) and help uplift team capability through practical coaching
To be successful in this role you should meet the following requirements:
- Strong software engineering experience delivering end-to-end services in production (not just notebooks/experiments), with ownership for run/support considerations
- Proficiency in Python and modern engineering practices (clean code, testing, packaging, dependency management, Git-based workflows)
- Hands-on experience with AI deployment patterns and infrastructure (e.g. containerisation with Docker, orchestration such as Kubernetes, API-based serving, batch/stream inference)
- Practical MLOps experience: CI/CD for ML, model packaging and release management, automated validation, monitoring, and lifecycle management
- Working knowledge of ML/DL frameworks and tooling (e.g. PyTorch/TensorFlow and the Python ML ecosystem) sufficient to collaborate effectively with data scientists and implement inference pipelines
- Experience working with complex, multi-layered datasets (including imbalanced data) and integrating data pipelines into AI services
- Hands-on experience building and deploying web APIs using libraries such as Flask or FastAPI.
- Proficiency with database technologies such as SQL Server or Postgres, etc.
- Strong stakeholder communication skills: able to explain technical designs, risks, and operational considerations to wide-ranging audiences
- Good organisational skills and delivery discipline (prioritisation, time management, working across multiple initiatives)
- Degree in a relevant field or equivalent practical experience (Masters highly preferred)
- Model explainability approaches/tools (e.g. SHAP) where required by use case and governance expectations (desirable)
Being open to different points of view is important for our business and the communities we serve. We're dedicated to creating diverse and inclusive workplaces - no matter their gender, ethnicity, disability, religion, sexual orientation, socio-economic background or age. We are committed to removing barriers and ensuring careers at our client are inclusive and accessible for everyone to be at their best. We take pride in being a Disability Confident Leader and will offer an interview to people with disabilities, long term conditions or neurodivergent candidates who meet the minimum criteria for the role.
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