Senior AI / Machine Learning Scientist

Archer; Greater London; In loco; GBP 90.000 - 130.000; Tempo pieno

Descrizione del lavoro

Archer, collaborating with HSBC, seeks a Senior Machine Learning Scientist to design, build, deploy, and operate production-grade AI services within Wholesale use cases. You will own end-to-end delivery, focusing on secure, scalable solutions and governance-aligned observability.

You will partner with data scientists and engineers to productionise PoCs, build robust MLOps pipelines, and contribute to engineering standards while coaching peers.

Competenze

• Production-grade AI/services design and deployment experience.
• Strong Python and ML/DL framework proficiency.
• Experience with MLOps and CI/CD for ML.
• Ability to translate requirements into scalable designs for production.
• Experience with data pipelines and APIs.

Mansioni

• Design, build, deploy, and operate production-grade AI services across Wholesale use cases.
• Productionise PoC/PoV work into hardened solutions with defined non-functional requirements and service ownership.
• Build and maintain MLOps pipelines (CI/CD, automated testing, packaging, model/version management) for repeatable releases.
• Develop reusable engineering assets (libraries, templates, reference architectures, infrastructure-as-code) to reduce technical debt.
• Implement observability for AI services (logging/metrics/tracing) and model performance monitoring.

Conoscenze

Python programming

MLOps

ML deployment

Containerization

Data pipelines

Formazione

Masters degree in a relevant field

Strumenti

Docker

Kubernetes

FastAPI

Flask

Git

SQL / PostgreSQL

Descrizione del lavoro

Archer is collaborating with HSBC to connect them with exceptional professionals for this role.

If you’re looking to take an exciting new direction with your HSBC career, an internal move can open the door to many opportunities, allowing you to take on a new challenge, and develop your skills. Bring your knowledge of our brand to a new role and grow yourself further.

We’re currently seeking an experienced professional to join our team in the role of 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.

A move across the business allows you to continue to access tailored professional development opportunities, and our fantastic benefits packages.

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)