VP - AI Engineering Lead

Barclays•Henderson, NV

1d•$150,000 - $230,000•Onsite

About The Position

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.

Requirements

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
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

Nice To Haves

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

Responsibilities

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 relationships in the data.
Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.
Lead the design and deployment of Generative AI and agentic systems that transform customer servicing, automate complex workflows, and significantly reduce operational demand.
Shape how LLM-driven capabilities are embedded into production platforms, delivering measurable impact across millions of customer interactions.