Lead Platform Engineer

Job Description 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.