The role focuses on designing, developing, and improving software using modern engineering approaches. The objective is to build technology capabilities that support business platforms and provide value to customers and colleagues.
Purpose of the Role
Develop and enhance software solutions using appropriate engineering methodologies. The work contributes to business platforms and technology services used across the organisation.
Accountabilities
- Develop and deliver reliable software solutions using industry aligned programming languages, frameworks, and tools while ensuring scalability, maintainability, and performance.
- Collaborate with product managers, designers, and engineers to define requirements, shape solution strategies, and support smooth integration with business objectives.
- Work closely with peers, participate in code reviews, and support knowledge sharing to maintain high code quality.
- Stay updated with emerging technologies and contribute to internal technology communities to encourage technical improvement.
- Follow secure coding standards to reduce vulnerabilities and safeguard sensitive information.
- Implement effective unit testing to maintain good code structure, readability, and reliability.
- Provide specialist advice and support to meet stakeholder and customer requirements.
- Complete assigned activities on time and to a high standard, contributing to both individual and team outcomes.
- Take responsibility for specific processes within the team.
- In some cases, guide or supervise team members by allocating work and coordinating resources.
- If acting as a people leader, demonstrate leadership behaviours that help colleagues succeed. These include listening authentically, energising others, aligning across the organisation, and developing team members.
- For individual contributors, manage workload independently and support the implementation of systems and processes within the work area.
- Execute work in line with defined procedures while collaborating with closely related teams.
- Review team outputs where required to ensure internal and stakeholder standards are met.
- Offer specialised knowledge and assistance related to assigned responsibilities.
- Take ownership of risk management and maintain strong controls in line with regulations and codes of conduct.
- Build understanding of how different teams contribute to the wider function and support collaborative outcomes.
- Develop knowledge of the principles and practices that underpin the work area and expand operational expertise.
- Use experience and established practices to make informed decisions and evaluate possible approaches when procedures do not fully apply.
- Communicate complex or sensitive information to customers where needed.
- Maintain relationships with stakeholders and customers to understand and address their needs.
Colleagues are expected to demonstrate Barclays values of Respect, Integrity, Service, Excellence, and Stewardship. They should also reflect the Barclays mindset by empowering others, challenging ideas constructively, and driving progress.
The role supports improvements in processes, reporting, and controls while contributing to business-as-usual activities within the Data and AI engineering function.
Technical Skills
- Basic to intermediate programming knowledge in Python.
- Understanding of machine learning concepts and exposure to libraries such as scikit-learn, TensorFlow, or PyTorch.
- Experience with Git-based version control platforms such as GitHub or GitLab.
- Basic awareness of CI/CD practices using tools like Jenkins or GitHub Actions.
- Exposure to cloud services, preferably AWS including services such as S3, EC2, or Lambda.
- Basic familiarity with containerisation technologies, particularly Docker.
- Understanding of software engineering principles including modular design, code readability, and testing practices.
- Interest in learning the AI and machine learning lifecycle, including model development, training, and deployment.
Additional Preferred Skills
- Exposure to data platforms or tools such as Snowflake, Databricks, or SQL.
- Familiarity with data engineering concepts including ETL pipelines, Airflow, or Spark fundamentals.
- Awareness of responsible AI practices, model interpretability, and governance principles.
- Exposure to generative AI ideas including prompt engineering or frameworks such as LangChain.
- Basic knowledge of API development and system integration.
- Understanding of DevOps practices and software delivery pipelines.
Qualifications
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related field