About the role
Job Description VP - AI Engineering Lead
Location: Whippany, NJ (On-site/Remote) or Henderson, NV (On-site/Remote)
Job Summary Lead the design and deployment of Generative AI and agentic systems to transform customer servicing, automate complex workflows, and reduce operational demand. Embed LLM-driven capabilities into production platforms for measurable impact across millions of customer interactions.
Required Skills & Experience
- Proven experience designing and deploying production-grade Generative AI systems (LLM-based apps using RAG, prompt orchestration, tool/agent integration)
- Hands-on expertise in building scalable AI/ML platforms in cloud environments (AWS or Azure) with MLOps best practices
- Architecting end-to-end AI pipelines (data ingestion, feature engineering, real-time/streaming with Kafka/Kinesis/Spark, low-latency inference)
- Proficiency in Python-based AI/ML development (PyTorch, TensorFlow, scikit-learn) plus GenAI-specific tooling
- Ability to lead engineering teams and deliver complex AI programs across product, architecture, and operations
Other Valued Skills
- Experience with LLM platforms (AWS Bedrock, Azure OpenAI) including evaluation, fine-tuning, safety guardrails, cost/performance optimization
- Familiarity with agentic orchestration and multi-step reasoning (virtual assistants, workflow automation)
- Containerization and scalable deployment (Docker, Kubernetes) for GenAI/ML workloads
- Responsible AI, model risk management, governance (explainability, bias mitigation, auditability in regulated environments)
- Translating ambiguous business problems into AI-driven solutions with measurable outcomes (cost efficiency, CX improvement, automation at scale)
Purpose of the Role Use innovative data analytics and machine learning to extract insights from bank data, inform strategic decision-making, improve operational efficiency, and drive innovation.
Key Accountabilities
- Identify, collect, and extract data from internal/external sources
- Perform data cleaning, wrangling, and transformation for quality and suitability
- Develop and maintain efficient data pipelines for automated acquisition and processing
- Design and conduct statistical and machine learning models
- Develop predictive models for forecasting risks and opportunities
- Collaborate with business stakeholders to add value through data science
Vice President Expectations Contribute to strategy, drive requirements, recommend change. Plan resources, budgets, policies. Manage processes, deliver improvements, escalate policy breaches. If managing a team: define roles, plan future needs, counsel on performance, contribute to pay decisions. Demonstrate leadership behaviours: Listen and be authentic, Energise and inspire, Align across the enterprise, Develop others. Manage risks, strengthen controls, understand organisation functions, collaborate across areas, create solutions based on analytical thought, build trusting relationships with stakeholders. Demonstrate Barclays Values (Respect, Integrity, Service, Excellence, Stewardship) and Mindset (Empower, Challenge, Drive).
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