Senior ML Scientist - Production AI & MLOps Lead

Archer; Greater London; Sur place; GBP 90 000 - 130 000; Plein temps

Résumé du poste

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.

Qualifications

• 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.

Responsabilités

• 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.

Connaissances

Python programming

MLOps

ML deployment

Containerization

Data pipelines

Formation

Masters degree in a relevant field

Outils

Docker

Kubernetes

FastAPI

Flask

Git

SQL / PostgreSQL

Description du poste

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.