VP - AI Engineering Lead

Date live: 07/15/2026

Business Area: Cards Platform

Area of Expertise: Technology

Contract: Permanent

Reference Code: JR-0000101566

Salary / Rate $ 170,000.00 - $ 230,000.00

Embark on a transformative journey as a VP - AI Engineering Lead. At Barclays, our vision is clear –to redefine the future of banking and help craft innovative solutions, AI-powered solutions at scale. In this role, you will lead the design and deployment of Generative AI and agentic systems that transform customer servicing, automate complex workflows, and significantly reduce operational demand. This is a unique opportunity to shape how LLM-driven capabilities are embedded into production platforms, delivering measurable impact across millions of customer interactions.

To be successful as a VP - AI Engineering Lead, you should have:

• Proven experience designing and deploying production-grade Generative AI systems, including LLM-based applications leveraging frameworks such as retrieval-augmented generation (RAG), prompt orchestration, and tool/agent integration
• Considerable hands-on expertise in building scalable AI/ML platforms in cloud environments (AWS or Azure), including model deployment, monitoring, and lifecycle management aligned to MLOps best practices
• Deep experience architecting end-to-end AI pipelines, including data ingestion, feature engineering, real-time/streaming architectures (Kafka, Kinesis, Spark), and low-latency inference systems
• Solid proficiency in Python-based AI/ML development (e.g., PyTorch, TensorFlow, scikit-learn) with ability to extend to GenAI-specific tooling and frameworks
• Demonstrated ability to lead engineering teams and deliver complex AI programs, partnering across product, architecture, and operations to drive real-world impact

Other highly valued skills include:

• Experience with LLM platforms and ecosystems (e.g., AWS Bedrock, Azure OpenAI), including evaluation, fine-tuning, safety guardrails, and cost/performance optimization
• Familiarity with agentic orchestration and multi-step reasoning systems, particularly in customer-facing or enterprise automation use cases (e.g., virtual assistants, workflow automation)
• Practical experience with containerization and scalable deployment (Docker, Kubernetes) for GenAI and ML workloads
• Good grounding in Responsible AI, model risk management, and governance, including explainability, bias mitigation, and auditability in regulated environments
• Ability to translate ambiguous business problems into AI-driven solutions, with focus on measurable outcomes such as cost efficiency, CX improvement, and automation at scale

You may be assessed on the key critical skills relevant for success in this role, such as risk and controls, change and transformation, business acumen, strategic thinking, digital and technology, as well as job-specific technical skills.

This role is located in our Whippany, NJ office or Henderson, NV.

Salary for Whippany, NY:

Minimum Salary: $170,000

Maximum Salary: $230,000

The minimum and maximum salary/rate information above includes only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.

Barclays employees are eligible for a suite of competitive and generous employee benefits, including medical, dental and vision coverage, 401(k), life insurance, and other paid leave for qualifying circumstances.

This position is eligible for an incentive award.

Salary for Henderson, NV:

Minimum Salary: $150,000

Maximum Salary: $210,000

The minimum and maximum salary/rate information above includes only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.

Purpose of the role

To use innovative data analytics and machine learning techniques to extract valuable insights from the bank’s data reserves, leveraging these insights to inform strategic decision-making, improve operational efficiency, and drive innovation across the organisation.

Accountabilities

• Identification, collection, extraction of data from various sources, including internal and external sources.
• Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
• Development and maintenance of efficient data pipelines for automated data acquisition and processing.
• Design and conduct of statistical and machine learning models to analyse patterns, trends, and relatio…