Senior Machine Learning Engineer

If you’re looking for a career that will help you stand out, join HSBC, 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’re currently seeking an experienced professional to join our team in the role of Senior Machine Learning Engineer.

As our Senior Machine Learning Engineer you’ll join the Growth AI programme within Corporate & Institutional Banking (CIB) Data & Analytics. Growth AI is a key pillar of the Insights and Analytics space within CIB Data & Analytics. Our focus is to develop AI applications that identify new business opportunities, identify risks of client attrition, and enhance our collective capabilities to serve our customers.

As an HSBC employee in the UK, you’ll have access to tailored professional development opportunities and a competitive pay and benefits package. This includes private healthcare for all UK-based employees, enhanced maternity and adoption pay and support when you return to work, and a contributory pension scheme with a generous employer contribution.

In this role you will:

• 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)
• Proven track record designing, deploying and operating production ML/GenAI services in cloud en