Lead Platform Engineer

Purpose of the role

To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues. 

Accountabilities

• Development and delivery of high-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance.
• Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives.
• Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing.
• Stay informed of industry technology trends and innovations and actively contribute to the organization’s technology communities to foster a culture of technical excellence and growth.
• Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions.
• Implementation of effective unit testing practices to ensure proper code design, readability, and reliability.

Director Expectations

• Provide expert advice to senior functional management and committees to influence decisions made outside of own function, offering significant input to function wide strategic initiatives.
• Manage, coordinate and enable resourcing, budgeting and policy creation for a significant sub-function.
• Escalates breaches of policies / procedure appropriately.
• Foster and guide compliance, ensure regulations are observed that relevant processes in place to facilitate adherence.
• Focus on the external environment, regulators, or advocacy groups to both monitor and influence on behalf of Barclays, when appropriate.
• Demonstrate extensive knowledge of how the function integrates with the business division / Group to achieve the overall business objectives.
• Maintain broad and comprehensive knowledge of industry theories and practices within own discipline alongside up-to-date relevant sector / functional knowledge, and insight into external market developments / initiatives.
• Use interpretative thinking and advanced analytical skills to solve problems and design solutions in often complex/ sensitive situations.
• Exercise management authority to make significant decisions and certain strategic decisions or recommendations within own area.
• Negotiate with and influence stakeholders at a senior level both internally and externally.
• Act as principal contact point for key clients and counterparts in other functions/ businesses divisions.
• Mandated as a spokesperson for the function and business division.

All Senior Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.

Key Responsibilities

• MLOps Leadership: Build and evolve model deployment pipelines, registries, and monitoring frameworks.
• Tooling & Integration: Implement and optimize SageMaker, Databricks, MLflow, and related orchestration tools.
• Governance & Risk: Ensure compliance with model governance, auditability, and AI safety standards.
• Operational Excellence: Drive reproducibility, lineage tracking, drift detection, and rollback strategies.
• Hands-On Delivery: Write production-grade Python code for automation, orchestration, and monitoring.
• Collaboration: Partner with data scientists, quants, and infra teams to align MLOps capabilities with business needs.
• Mentorship: Coach engineers on best practices for ML lifecycle management and secure deployment.

Required Skills

• Strong Python engineering background.
• Deep experience with SageMaker, Databricks, MLflow, and CI/CD for ML.
• Proven track record in operationalizing ML models at scale.
• Knowledge of model governance, compliance, and risk management.
• Familiarity with feature stores, model registries, and observability tools (Langfuse, Observe).

Preferred Tech

• Kubernetes (EKS/on-prem), Airflow, Step Functions for orchestration.
• Databricks for data engineering and ML pipelines.
• MLflow for experiment tracking and model registry.
• SageMaker for training and deployment.
• Langfuse for evaluation and tracing.