AI Engineering Lead-VP

What matters most

• Evolve the AWS AI platform

  The role calls for hands-on leadership in designing, building and maintaining scalable, secure AWS infrastructure for ML, generative AI and agentic workloads, alongside platform services and deployment pipelines.

• Treat the platform as a product

  Barclays explicitly emphasizes outcomes, scalability, self-service, continuous evolution and user experience. Focus on how platform decisions serve users and business objectives, not just technical delivery.

• Lead teams and delivery

  The accountabilities include building high-performing teams, mentoring and performance feedback, and managing timelines, allocation, priorities, risks and budgets. VP expectations also include strategic and resource planning.

• Align stakeholders and strengthen controls

  The role works across security, governance, architecture, product and business teams to turn requirements into solutions. It also calls for advising senior stakeholders and taking accountability for risk and controls.

• Raise engineering quality and efficiency

  Barclays calls for improved processes and workflows, technology standards, peer reviews and robust testing. It also mentions CI/CD, Infrastructure as Code and structured, reusable patterns when using AI development tools such as Claude Code.

Likely challenges to think through

• Self-service versus governance

  A likely trade-off is making AI infrastructure easy to use while meeting security and control needs; the posting does not identify a specific current problem. Prepare to explain how you would involve governance partners, define appropriate guardrails and assess residual risk.

• Shared platform versus varied AI workloads

  ML, generative AI and agentic systems may have different deployment and operational needs. Prepare to discuss where reusable platform services make sense, where workload-specific choices may be necessary, and how you would evaluate those alternatives.

• Delivery pace versus sustainable quality

  Timelines and budgets may compete with testing, standards and team development; this is a preparation hypothesis, not a reported issue at Barclays. Prepare an approach to prioritizing work, making trade-offs visible to stakeholders and protecting critical controls.

Topics to prepare

• AI workload architecture

  The posting explicitly covers AWS platforms for ML, generative AI and agentic workloads. Revise by comparing the infrastructure, deployment and security considerations of each workload in a one-page decision table.

• Platform-as-product thinking

  The role calls for outcomes, scalability, self-service, continuous evolution and user experience. Revise by defining a hypothetical platform user, their self-service journey and three measures of whether the platform improves their experience.

• Enterprise risk and controls

  The posting emphasizes security, governance, risk assessment, controls and escalation of policy breaches. Revise by tracing a hypothetical AI platform change from stakeholder requirements through risk review, approval and escalation, noting where decisions need evidence.

• VP-level engineering delivery

  The role includes strategy, resource and budget planning, prioritization, mentoring and performance feedback. Revise by working through a scenario with competing delivery deadlines and limited capacity; explain the trade-offs, risks and how you would communicate the decision.

• Engineering quality and change

  The accountabilities include technology standards, peer reviews, testing and improving engineering workflows. Revise by outlining review and testing criteria for a platform change, then identify one process improvement and how its effect could be measured.

Skills to revise

• Design an AWS AI platform

  Hands-on AWS platform design and maintenance are explicit requirements. Sketch a scalable, secure platform for a hypothetical AI workload; annotate deployment boundaries, operational responsibilities and the architectural trade-offs you would defend.

• Plan an automated deployment

  The posting specifies exposure to CI/CD pipelines and Infrastructure as Code deployments. Draft a sample change flow from infrastructure definition through review, testing, deployment and rollback, including where controls would be checked.

• Translate cross-functional requirements

  The role works across security, governance, architecture, product and business stakeholders. Practice turning a short hypothetical business request into technical requirements, open questions, risks and a concise recommendation for senior stakeholders.

• Coach and allocate an engineering team

  Building high-performing teams, mentoring, performance reviews and team allocation are stated responsibilities. Rehearse giving specific feedback on a hypothetical design review, then allocate a small team across two competing priorities and explain your reasoning.

• Apply structured AI-tool rules

  The posting names Claude Code and structured rules and reusable patterns for accelerating development. Draft rules for a small platform-code change, use them in a trial if the tool is available, and inspect the proposed changes against review and testing criteria.

• Prototype an AI application

  Building ML models and developing generative AI applications are described as highly valued, not essential. As optional preparation, build or outline a small example and explain how you would test it and deploy it through the platform.

• Build an AWS platform walkthrough

  Prepare a diagram and concise account of an AI workload platform you worked on, covering your decisions, contribution, scale, security and operational outcomes. The role calls for hands-on AWS experience supporting ML, GenAI or agentic workloads.

• Practice a platform design exercise

  As interview practice, sketch a scalable, secure AWS platform that takes an AI workload from development to deployment. Explain trade-offs, likely risks and how platform services could support self-service; do not assume Barclays uses a particular architecture.

• Collect delivery and quality evidence

  Prepare specific examples of CI/CD and Infrastructure as Code deployments, plus peer review, testing or engineering-process improvements. Note what changed and any measurable result you can substantiate.

• Outline a product-oriented platform plan

  Draft a short, hypothetical plan with user needs, desired outcomes, incremental priorities and ways to assess scalability and user experience. The posting emphasizes self-service, continuous evolution and business alignment.

• Rehearse a team-leadership example

  Choose an example showing how you developed engineers while managing priorities, allocation, timelines, budget or delivery risk. Be ready to explain decisions, feedback given and outcomes without implying a particular team structure for this role.

• Map a stakeholder and controls decision

  Prepare a case in which you worked across security, governance, architecture and business stakeholders to resolve competing requirements. Show how you assessed risk, strengthened controls and advised decision-makers.

• Prepare an AI-assisted development example

  If applicable, show how you used a tool such as Claude Code with structured rules or reusable patterns, including review safeguards. Separately identify any ML-model, GenAI-application or iterative Agile experience; the posting describes these as highly valued, not mandatory.

• Write questions for the hiring manager

  Bring questions about current platform priorities, success measures, team leadership scope, security and governance decision-making, and the Pune role’s fixed on-site anchor days. These clarify details the posting does not settle.