AI Platform Engineer

What matters most

• Lead SOLD Simplification

  The posting makes this multiyear initiative central to the role: lead its engineering and development across platform compliance, hardware refresh and removal of redundant functionality. Prepare to explain how you would sequence changes and track delivery risks.

• Turn requirements into a delivery roadmap

  The role coordinates requirements discovery, planning, the roadmap and stakeholder management. It also calls for managing timelines, allocation, priorities and budget, and advising senior stakeholders. Prepare examples of decisions made across business and engineering teams.

• Set cloud platform direction

  The posting calls for cloud platform engineering, with Azure preferred, plus Kubernetes, containerisation, Terraform or Bicep, and platform operations. Be ready to explain architectural choices and operational consequences rather than naming tools alone.

• Operationalise AI services

  Required experience spans scalable Python or Java services and APIs alongside LLM deployment, model lifecycle management, prompt orchestration, vector databases and AI observability. Prepare to describe how these components work together in a maintainable platform.

• Uphold engineering quality and controls

  The role calls for technology standards, peer reviews and robust testing, with DevSecOps requirements covering CI/CD, secrets, vulnerability remediation and secure-by-design practices. Risk and controls are also named as possible assessment areas.

Likely challenges to think through

• Modernising without losing needed functionality

  Preparation hypothesis, not a stated employer problem: refreshing hardware and removing redundant functionality while meeting compliance aims may force trade-offs between speed and continuity. Prepare an approach to discovering dependencies, defining removal criteria and sequencing changes with risk checks.

• Balancing AI delivery with control requirements

  Preparation hypothesis: operationalising LLMs may create tension between rapid iteration and security, testing and observability. Prepare to explain release gates, monitoring and remediation decisions; AI governance and model risk controls are listed as highly valued, not mandatory.

• Leading across an uncertain reporting structure

  Preparation hypothesis: the posting describes both people-leader and individual-contributor routes at Vice President level, so influence may matter with or without direct reports. Prepare examples of resolving competing stakeholder priorities, guiding technical decisions and escalating risks; do not assume a specific team structure.

Topics to prepare

• SOLD Simplification and change trade-offs

  The posting requires experience with SOLD Simplification and describes a multiyear effort involving platform compliance, hardware refresh and removal of redundant functionality. Revise by outlining how you would sequence those three changes, identify dependencies and explain the principal delivery risks without assuming undocumented details of SOLD.

• Cloud platform architecture

  Cloud platform engineering, Kubernetes, containerisation, Infrastructure as Code and operations are stated requirements; Azure is preferred. Revise by drawing a containerised platform architecture and explaining its deployment, configuration and operational boundaries.

• Scalable services and integration

  The role calls for Python and/or Java engineering, scalable services, APIs and integration frameworks. Revise by designing an API contract and explaining how you would handle scale, integration failures and testing.

• LLM platform lifecycle

  LLM operationalisation, model lifecycle management, prompt orchestration, vector databases and AI observability are explicit requirements. Revise by tracing a model from deployment through monitoring and change, noting what each component contributes.

• DevSecOps and enterprise controls

  The posting specifies CI/CD, automated testing, secrets management, vulnerability remediation and secure-by-design practices. Revise by mapping the controls you would apply from a code change through deployment and explaining how a discovered vulnerability would be handled.

• Engineering leadership and delivery governance

  The role leads engineering delivery and coordinates requirements, roadmap, stakeholders, resources and risks. Revise by working through a hypothetical scope-versus-timeline conflict: state the options, control implications, recommendation and stakeholder communication.

Skills to revise

• Draft a Kubernetes and IaC deployment

  Practice writing a small container deployment and a Terraform or Bicep outline, then explain how you would operate and update it. This rehearses the posting’s cloud, Kubernetes and Infrastructure as Code requirements; Azure is preferred, not mandatory.

• Implement and test an API

  Practice building a small service in Python or Java with an API endpoint, an integration boundary and automated tests. Explain design choices for scalability and failure handling, as the posting requests scalable services and APIs.

• Walk through an LLM service change

  Practice specifying an LLM deployment change, its prompt orchestration and vector-database interactions, and the signals you would observe before and after release. This targets the stated MLOps and AI-observability work; the exercise is a preparation suggestion, not a specified Barclays workflow.

• Review a secure delivery pipeline

  Practice sketching CI/CD stages with tests, secrets handling and a vulnerability-remediation step. Explain what would block a release and why, tying the exercise to the stated DevSecOps and enterprise-security requirements.

• Turn requirements into a delivery plan

  Practice converting a hypothetical stakeholder request into technical requirements, milestones, team priorities and a risk register. This reflects the advertised requirements discovery, roadmap coordination and responsibility for delivery within scope, time and budget.

• Map your experience to SOLD Simplification

  The posting explicitly requires SOLD Simplification experience. Prepare a factual account of any relevant work on platform compliance, hardware refresh or removal of redundant functionality; do not present adjacent work as direct experience.

• Build a platform project walkthrough

  Prepare one real example showing requirements discovery, technical decisions and delivery outcomes. Cover cloud platform operations, Kubernetes, containerisation and Infrastructure as Code where they were part of the work; Azure is preferred in the posting.

• Practise a platform design exercise

  Sketch how you would deploy and operate a scalable AI service, including infrastructure, a Python or Java API, integration points and trade-offs. Treat the sketch as your proposed approach, not Barclays’ existing architecture.

• Prepare an MLOps lifecycle example

  Walk through a real LLM deployment or comparable AI platform project, covering model lifecycle decisions, prompt orchestration, vector databases and observability where applicable. Identify clearly which parts you designed or operated.

• Show how you manage security and controls

  Prepare evidence of CI/CD, automated testing, secrets management, vulnerability remediation and secure-by-design decisions. Add model-risk controls or AI guardrails if you have relevant examples; these are listed as highly valued skills.

• Rehearse a senior-level delivery account

  Prepare a concise example of turning stakeholder requirements into a roadmap while handling priorities, team allocation, budget or schedule constraints, risks and escalation. Explain how you guided specialists or mentored engineers without assuming this position is a people-manager role.

• Draft questions for the hiring manager

  Ask about the SOLD Simplification scope and success measures, current AI platform and operational priorities, leadership expectations, and the Pune role’s fixed onsite anchor-day pattern. These questions clarify details the posting does not specify.