SDLC & Responsible AI Acceleration Apprentice

SCOR is seeking an apprentice to support the definition and improvement of its Software Development Life Cycle (SDLC) practices, with a specific focus on how Artificial Intelligence can accelerate IT projects while maintaining strong control over quality, security, compliance, and operational risk.

The mission will contribute to identifying practical AI use cases across the IT delivery lifecycle, from requirements clarification and backlog preparation to development support, testing, documentation, release preparation, and run activities.

The apprentice will help design a pragmatic framework to assess where AI can bring measurable
productivity gains, which controls must remain mandatory, and how human oversight, traceability, architecture standards, cybersecurity, and governance principles should be embedded into AI-assisted delivery.

This role is an opportunity to work at the intersection of IT project delivery, Agile practices, DevOps, software engineering governance, responsible AI, and risk management, in close collaboration with project managers, product owners, architects, developers, security teams, and data/AI stakeholders.

The expected outcome is to help SCOR accelerate IT delivery in a responsible, secure, auditable, and scalable way, ensuring that AI supports teams without bypassing essential engineering, validation, and governance controls.

SDLC Assessment and Improvement

• Map the current IT project delivery lifecycle, including requirements, design, development, testing, deployment, documentation, and run activities.
• Identify pain points, manual activities, quality gates, handovers, and recurrent bottlenecks that slow down IT delivery.
• Contribute to recommendations to make the SDLC more efficient, consistent, traceable, and easier to apply across project teams.
• Help document practical delivery standards, templates, checklists, and governance artefacts to support project teams.

Responsible AI Use Cases for IT Delivery

• Identify where AI can responsibly support project teams, for example in requirements analysis, user story drafting, impact analysis, code assistance, test cae generation, documentation, knowledge management, and project reporting.
• Assess expected value, feasibility, prerequisites, risks, and required safeguards for each AI-assisted SDLC use case.
• Define practical guidance for using AI tools without compromising confidentiality, security, intellectual property, auditability, or engineering quality.
• Promote a balanced approach where AI accelerates work but does not replace mandatory reviews, validation, testing, security controls, or human decision-making. Risk, Governance and Control Framework
• Contribute to a lightweight risk framework for AI-assisted SDLC activities, including use case classification, risk assessment, mandatory controls, and escalation criteria.
• Define how traceability, documentation, review evidence, validation results, and approval steps should be captured when AI is used in IT delivery.
• Work with architecture, cybersecurity, data protection, compliance, and operations stakeholders to ensure that proposed practices are safe, realistic, and aligned with enterprise standards.
• Support the definition of KPIs to measure acceleration benefits, quality impact, adoption, control effectiveness, and residual risk.

Experimentation, Tooling and Knowledge Sharing

• Support controlled experiments and proofs of concept on AI-assisted SDLC use cases, with clear success criteria and risk controls.
  o AI-assisted requirements clarification and user story drafting
  o AI-supported code review, test generation, and documentation
  o Project reporting, RAID log preparation, and delivery dashboard support
  o Knowledge base improvement, lessons learned, and reusable delivery playbooks
  o Controlled evaluation of AI productivity tools under appropriate governance
• Prepare communication material, guidance notes, and practical examples to help IT teams adopt responsible AI practices.
• Contribute to awareness sessions and communities of practice around SDLC improvement, AI adoption, and risk-aware delivery.

Collaboration and Delivery Support

• Work closely with IT project managers, product owners, Scrum Masters, developers, architects, security experts, and data/AI specialists.
• Support workshops, interviews, process mapping sessions, and working groups related to SDLC improvement and AI enablement.
• Prepare clear deliverables such as process maps, analysis notes, presentations, templates, guidance documents, and executive summaries.
• Help translate technical and governance concepts into simple, actionable recommendations for project teams.

Expected Deliverables

• Current-state assessment of selected SDLC processes and opportunities for AI-assisted acceleration.
• AI use case catalogue for IT delivery, including value, risks, controls, prerequisites, and implementation recommendations.
• Responsible AI for SDLC playbook, including governance principles, practical guidelines, templates, KPIs, and adoption roadmap.

Required experience & competencies

Experience

• First experience or strong academic exposure to IT project delivery, software engineering, Agile, DevOps, data, AI, cybersecurity, risk management, or digital transformation.
• Interest in how AI can improve productivity across the software development lifecycle while preserving quality, security, compliance, and governance.
• Ability to analyze processes, structure information, prepare clear documentation, and translate complex topics into practical recommendations.
• Good understanding of IT delivery concepts such as requirements, backlog, testing, release management, documentation, architecture, and operational support.
• Curiosity for generative AI, responsible AI, automation, software quality, and technology governance.
• Experience working with business stakeholders and cross-functional leadership teams.

Competencies

o Technical : Strong knowledge of:

• Software Development Life Cycle principles and Agile delivery practices
• Generative AI opportunities, limitations, responsible use, and human oversight principles
• IT risk management, cybersecurity awareness, data protection, auditability, and governance controls
• Process analysis, documentation, stakeholder interviews, workshop facilitation, and change support
• Knowledge of cloud, DevOps, coding, data platforms, or enterprise architecture is a plus.

o Behavioural

• Collaboration
• Courage
• Open-mindedness
• Innovation mindset
• Results orientation
• Strong communication and presentation skills

Required Education

• Master’s degree in progress in Computer Science, Engineering, Information Systems, Digital Transformation, Data/AI, Cybersecurity, Project Management, or a related analytical field, as part of an apprenticeship program.