Senior Machine Learning Engineer

HSBC Group; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

HSBC Group in the United Kingdom seeks a senior AI/ML engineer to design, build, deploy, and operate production-grade AI services. You will implement observability, monitoring, and lifecycle management for GenAI/ML workloads, ensuring security, performance, and cost efficiency.

You will collaborate with data scientists, data engineers, and governance teams to align technical delivery with risk and regulatory expectations.

Qualifications

• End-to-end production ML/GenAI delivery experience.
• Strong Python and modern engineering practices.
• Experience with MLOps pipelines and CI/CD.

Responsibilities

• Design, build, deploy, and operate production-grade AI services.
• Implement observability, monitoring, and alerting.
• Collaborate with data scientists, platform teams, governance and risk stakeholders.
• Translate business requirements into technical designs.

Skills

Python

ML/GenAI experience

Docker/Kubernetes

CI/CD for ML

Observability/Monitoring

Tools

Docker

Kubernetes

Flask/FastAPI

SQL/PostgreSQL

Git workflows

Job description

• 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‑to 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
  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.

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 realisetheir ambitions., Being open to different points of view is important for our business and the communities we serve. At HSBC, 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 HSBC 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.
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.