New opportunity

GenAI Data Engineering Lead

Barclays · Pune, India

About this role

The GenAI Data Engineering Lead at Barclays leads engineering teams delivering technology and generative AI solutions from Pune. The role supports delivery of location strategy projects to plan, budget, quality and governance standards, while providing technical guidance and aligning engineering decisions with business objectives.

Responsibilities

Lead, mentor and develop software, front-end and AI engineers; support performance reviews, feedback and professional growth.
Manage project timelines, team allocation, priorities, budgets and risks to deliver solutions within scope. Improve engineering processes, tools and workflows.
Define and enforce engineering standards, facilitate peer reviews and robust testing, and drive scalable, secure and resilient solutions.
Work with product owners, architects, designers, business partners and other stakeholders to translate requirements into technical solutions and execute technology roadmaps. Champion AI, automation and cloud-native innovation.
Contribute to strategy, resource and policy decisions, advise senior stakeholders, and strengthen risk controls and governance.

Required experience and skills:

Strong leadership experience managing software engineering teams and hands-on Python development using FastAPI, Django, APIs and microservices, alongside React.
Experience building generative AI solutions involving LLMs, AI assistants, retrieval-augmented generation (RAG), AI agents or agentic workflows, prompt engineering, and GenAI governance and responsible AI.
Experience with AWS, containers and CI/CD. No specific number of years or education requirement is stated.

The posting additionally values team development, engineering standards, stakeholder partnership and delivery of secure, resilient technology. The role is permanent and full-time. Barclays describes a hybrid working approach with fixed on-site anchor days set by the business area; applicants should discuss the role's working pattern with the hiring manager. Listed benefits include a competitive holiday allowance, life assurance, private medical care and a pension contribution.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Engineering leadershipPythonFastAPIDjangoAPIsMicroservicesReactGenerative AILarge language modelsAI assistantsRAGAI agentsAgentic workflowsPrompt engineeringGenAI governanceResponsible AIAWSContainersCI/CDRisk managementStakeholder management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Lead and develop engineering teams
The role leads software, front-end and AI engineers, with responsibility for mentoring, feedback, performance reviews and a collaborative, high-performing team.
Deliver within agreed constraints
The posting calls for managing timelines, team allocation, priorities and risks so location strategy projects meet scope, budget, quality and governance standards.
Guide GenAI solution development
Barclays seeks experience building solutions involving LLMs and AI assistants, RAG, agents or agentic workflows, and prompt engineering, alongside GenAI governance and responsible AI.
Set technical quality standards
The lead provides technical guidance, enforces standards, facilitates peer review and robust testing, and drives scalable, secure and resilient solutions. The stated engineering experience spans Python APIs and microservices, React, AWS, containers and CI/CD.
Align engineering with business and controls
The role translates requirements with product owners, architects and business partners, advises senior stakeholders, contributes to strategy and resource decisions, and takes accountability for risk and controls.

Likely challenges to think through

Balancing GenAI innovation with governance
Likely preparation challenge, not a confirmed Barclays problem: advancing AI assistants or agentic workflows while meeting responsible-AI, quality and control expectations. Prepare a concrete account of how you would assess alternatives, test a solution and make risks visible before delivery.
Protecting quality under delivery pressure
Likely preparation challenge: scope, time and budget constraints can compete with peer review, testing, security and resilience. Prepare to explain how you would prioritize work, allocate engineers and escalate a material risk without treating standards as optional.
Keeping technical and business decisions aligned
Likely preparation challenge: product, architecture and business stakeholders may favor different outcomes, while the lead must also guide engineers. Prepare an example of comparing technical options, explaining trade-offs to senior stakeholders and turning an agreed direction into team priorities.

Practice your answers

Technical practice

How would you structure a Python API for a GenAI assistant?

What to coverThe role calls for hands-on Python, FastAPI, Django, APIs and microservices experience. Preparation suggestion: explain how you would define service boundaries, API contracts and error handling, and when a separate service would be worth its operational cost.

How would you connect a React interface to a GenAI service?

What to coverThe posting names React alongside Python APIs and AI assistants. Preparation suggestion: discuss how the interface would handle requests, display responses and communicate failures while keeping responsibilities clear between front end and back end.

How would you design and evaluate a RAG workflow?

What to coverRAG is explicitly listed among the GenAI solutions for this role. Preparation suggestion: walk through retrieval and answer generation, then explain how you would investigate irrelevant retrieval or unsupported answers and judge whether changes improve results.

How would you improve prompts for an LLM-based assistant?

What to coverThe posting specifically names LLMs, AI assistants and prompt engineering. Preparation suggestion: describe how you would set instructions and response boundaries, compare prompt revisions against representative tasks and account for unreliable outputs.

When would an agentic workflow be preferable to a simpler assistant?

What to coverAI agents and agentic workflows are stated experience areas. Preparation suggestion: compare the value of multi-step actions with their added complexity, and explain how you would limit actions and handle failures.

What controls would you put around a GenAI solution?

What to coverGenAI governance, responsible AI, risk and controls are explicit themes in the posting. Preparation suggestion: discuss how you would identify risks, set appropriate safeguards and review whether the solution remains within its intended use.

How would you deliver a containerised service on AWS through CI/CD?

What to coverThe role calls for AWS, containers and CI/CD experience. Preparation suggestion: outline build, test, deployment and rollback decisions, including how you would balance release speed with control and reliability.

How would you make a GenAI service secure and resilient?

What to coverThe posting values scalable, secure and resilient solutions and calls for robust testing. Preparation suggestion: explain likely failure points across the API and AI components, how you would test them and what trade-offs you would make under load.

How would you establish engineering standards across different disciplines?

What to coverThe role covers software, front-end and AI engineers, with responsibility for standards and peer reviews. Preparation suggestion: discuss which practices should be consistent across teams, where discipline-specific review is needed and how you would check that standards improve quality.

How would you turn business requirements into a technical roadmap?

What to coverThe posting calls for partnership with product owners, architects and business stakeholders to translate requirements and execute roadmaps. Preparation suggestion: explain how you would clarify outcomes, compare technical alternatives and make trade-offs visible to decision-makers.

Behavioural practice

Tell me about a time you developed an engineer through feedback.

What to coverMentoring, professional growth and actionable performance feedback are stated accountabilities. Preparation suggestion: use a real situation, the feedback and support you provided, and the observable result; avoid claiming an outcome you cannot substantiate.

Describe a time you had to rebalance delivery priorities under constraints.

What to coverThe role oversees timelines, team allocation, risks and priorities while delivering within scope and budget. Preparation suggestion: describe the situation, how you made and communicated trade-offs, and what happened to delivery and risk.

Give an example of influencing stakeholders with competing objectives.

What to coverThe posting emphasises cross-functional partnership and advice to senior stakeholders. Preparation suggestion: set out the differing objectives, your actions to reach a decision and the resulting outcome or remaining compromise.

Tell me about a risk or control issue you identified and addressed.

What to coverThe role calls for risk assessment, stronger controls and escalation of policy or process breaches. Preparation suggestion: explain the situation, how you assessed and raised the issue, the action taken and the result without implying every risk was eliminated.

Describe an engineering-process change you led.

What to coverEvaluating processes and workflows for continuous improvement is an explicit accountability. Preparation suggestion: describe the initial problem, how you brought the team along, what you changed and the evidence you used to judge its effect.

Sharpen your knowledge

Topics to prepare

LLM solution patterns
The posting names LLMs, AI assistants, RAG, agents and agentic workflows. Revise when an assistant should retrieve information, invoke an action or answer directly; sketch the information flow and failure points for each choice.
Prompt engineering and responsible AI
Prompt engineering, GenAI governance and responsible AI are explicit requirements. Compare two prompts for the same assistant task, then identify how you would evaluate inaccurate, unsafe or inappropriate responses and govern changes.
Secure and resilient engineering architecture
The role values scalable, secure and resilient solutions and requires APIs and microservices experience. Sketch service boundaries for a GenAI application and explain how you would handle failures, access and testing; this architecture exercise is a preparation suggestion, not a specified project.
Engineering leadership and delivery controls
The role calls for team leadership and oversight of timelines, allocation, priorities, budgets and risk. Work through a hypothetical delayed delivery: decide what to reprioritize, what to escalate and how to preserve quality and governance standards.
Stakeholder alignment and strategy
The posting expects partnership with product owners, architects and business stakeholders, plus advice to senior stakeholders. Practise explaining two technical options in terms of business objectives, delivery trade-offs and risks.

Skills to revise

Build and review Python APIs
The posting requires hands-on Python with FastAPI, Django, APIs and microservices. As a revision exercise, implement a small endpoint in FastAPI or Django, add tests and review its interface and error handling; be ready to explain how it fits a microservice.
Connect a React interface
React is an explicit hands-on requirement. Build a simple React screen that calls your practice API, handles loading and error states, and explain the front-end/back-end interface.
Prototype a GenAI workflow
The posting calls for experience building assistants, RAG, agents and prompts. Prototype a small assistant workflow using sample content: write prompts, show when retrieval or an agent action occurs, and test answers against the source content. This is a revision exercise, not an advertised Barclays project.
Plan a containerized delivery path
AWS, containers and CI/CD are named requirements. Containerize the practice API and outline a CI/CD sequence for testing and deploying it on AWS, including a rollback decision; the posting does not prescribe a particular AWS service or pipeline tool.
Run a technical review and delivery check
Peer reviews, robust testing, mentoring and risk management appear in the accountabilities. Practise reviewing a sample change, recording actionable feedback, then drafting a short delivery update covering progress, quality risks and decisions needed from stakeholders.

Your preparation checklist

Prepare a team-leadership example
Outline a real example of leading software engineers: team scope, mentoring or feedback, a delivery challenge, decisions made and observable results. The role explicitly calls for team leadership and professional development.
Build a hands-on engineering walkthrough
Prepare a project walkthrough showing your contributions to Python APIs or microservices, using FastAPI or Django where applicable, and React where relevant. Be ready to explain design choices, testing and what you personally built.
Explain a GenAI solution end to end
Use a real example, if available, to show how an LLM assistant, RAG or agentic workflow was designed and evaluated. Identify your prompt choices, limitations and responsible-AI or governance controls; do not imply one project covered every listed technology.
Practice a secure architecture exercise
As self-directed preparation, sketch a hypothetical scalable service using Python, React, AWS, containers and CI/CD. Annotate security, resilience, peer review and testing decisions, including trade-offs rather than assuming this is an interview exercise.
Document a delivery and risk decision
Prepare a concise example of balancing timelines, team allocation, budget, quality and governance. Show the risk you identified, the control or escalation you chose, and how you communicated the decision.
Rehearse a stakeholder recommendation
Draft a short account of translating business requirements into a technical roadmap with product owners, architects or business partners. Explain alternatives considered and how the recommendation aligned engineering work with business objectives.
Write targeted questions for the hiring manager
Ask about the scope of the location strategy projects, the team's GenAI governance priorities, leadership expectations and the Pune role's fixed onsite anchor days. These are clarification questions, not assumptions about how the team operates.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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