2 days ago

Senior Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

Design, build, deploy and operate secure, scalable production-grade ML/GenAI services within the Growth AI programme. Productionise AI prototypes with clear non-functional requirements, and maintain MLOps/LLMOps pipelines for repeatable releases. Develop reusable engineering assets, implement observability and model quality/drift monitoring, and partner across data science, engineering, and governance stakeholders to deliver end-to-end, auditable solutions for wholesale business use cases.

Location: London

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•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 non-functional requirements and defined service ownership.
•Build and maintain MLOps/LLMOps pipelines, including CI/CD, automated testing, packaging, promotion/rollback, and model/version management.
•Implement observability for AI services with 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; translate requirements into technical designs and communicate trade-offs to technical and non-technical stakeholders.

Pay and Benefits

Perks:Health InsurancePensionPaid Leave

Key Requirements

•Strong software engineering experience delivering end-to-end production services (not just experiments), with ownership for run/support considerations.
•Proficiency in Python and modern engineering practices including testing, packaging, dependency management, and Git-based workflows.
•Hands-on experience deploying AI using patterns such as Docker containerisation, Kubernetes orchestration, API-based serving, and batch/stream inference.
•Practical MLOps experience including CI/CD for ML, model packaging/release management, automated validation, monitoring, and lifecycle management.
•Working knowledge of ML/DL frameworks and tooling (e.g., PyTorch/TensorFlow) sufficient to collaborate on inference pipeline implementation.

Experience:BankingFinancial servicesGenAIMachine learning

Skills:CommunicationStakeholder managementOrganisationDelivery disciplineCoaching

Tech Stack:PythonDockerKubernetesCI/CDMLOpsLLMOpsGitFlaskFastAPISQL ServerPostgresPyTorchTensorFlowRAGGenAIAPI-based servingModel/version managementObservabilityLoggingMetrics

Company Brief

HSBC

Global banking and financial services organisation offering retail, commercial, corporate and investment banking, wealth management, and global markets services across Europe, Asia, the Americas and the Middle East.

Industry: Banking

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: London, United Kingdom

Founded: 1865

Glassdoor: 3.6

Website LinkedIn

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HSBC

2 days ago

Senior Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 13 hours agoStatus: Live

Job Summary

Location: London

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•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 non-functional requirements and defined service ownership.
•Build and maintain MLOps/LLMOps pipelines, including CI/CD, automated testing, packaging, promotion/rollback, and model/version management.
•Implement observability for AI services with 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; translate requirements into technical designs and communicate trade-offs to technical and non-technical stakeholders.

Pay and Benefits

Perks:Health InsurancePensionPaid Leave

Key Requirements

•Strong software engineering experience delivering end-to-end production services (not just experiments), with ownership for run/support considerations.
•Proficiency in Python and modern engineering practices including testing, packaging, dependency management, and Git-based workflows.
•Hands-on experience deploying AI using patterns such as Docker containerisation, Kubernetes orchestration, API-based serving, and batch/stream inference.
•Practical MLOps experience including CI/CD for ML, model packaging/release management, automated validation, monitoring, and lifecycle management.
•Working knowledge of ML/DL frameworks and tooling (e.g., PyTorch/TensorFlow) sufficient to collaborate on inference pipeline implementation.

Experience:BankingFinancial servicesGenAIMachine learning

Skills:CommunicationStakeholder managementOrganisationDelivery disciplineCoaching

Tech Stack:PythonDockerKubernetesCI/CDMLOpsLLMOpsGitFlaskFastAPISQL ServerPostgresPyTorchTensorFlowRAGGenAIAPI-based servingModel/version managementObservabilityLoggingMetrics

Company Brief

HSBC

Industry: Banking

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: London, United Kingdom

Founded: 1865

Glassdoor: 3.6

Website LinkedIn