Description
We are 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.
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 environments, understanding the operational realities vs on‑prem (security, networking, scaling, resilience and cost management)
- Hands on experience building agentic LLM systems, including RAG workflows, tool/function calling and orchestration, and integrating external APIs and enterprise data sources to improve business operations with appropriate guardrails and evaluation