AVP - GenAI + ML Engineer (116333)

Purpose of the role

To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure. 

Accountabilities

• Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
• Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
• Development of processing and analysis algorithms fit for the intended data complexity and volumes.
• Collaboration with data scientist to build and deploy machine learning models.

Assistant Vice President Expectations

• To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
• Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
• OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
• Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
• Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
• Take ownership for managing risk and strengthening controls in relation to the work done.
• Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
• Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
• Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience.
• Influence or convince stakeholders to achieve outcomes.

• Own the end-to-end product lifecycle for Generative AI, Agentic AI, AI/ML solutions, from design to deployment.
• Act as a bridge between business and technology teams, ensuring alignment of requirements and technical specifications.
• Enable data-driven insights and AI-powered automation for Conversational AI, GenAI, and ML use cases.

Key Responsibilities

• Design & Implement Agentic, GenAI Solutions: Build robust Generative AI and Agentic AI applications using AWS Bedrock, SageMaker, and other AWS ML services.
• Develop LLM bases Use Cases: Architect and implement LLM based use cases like RAG, Summarization, data analysis etc. for enterprise-scale applications.
• LLM Finetuning & Evaluation: Fine-tune, evaluate, and optimize Large Language Models (LLMs) for performance, accuracy, and safety.
• Integration & Collaboration: Work closely with product managers, engineers, and UX teams to embed AI capabilities into business workflows.
• Innovation & Research: Stay ahead of AI trends, frameworks, and best practices; apply them to drive continuous innovation.
• Quality & Governance: Maintain system design integrity, review test strategies and ensure compliance with AI ethics and security standards.
• Optimize solutions through thorough research experimentation, and advanced problem-solving techniques.
• Communicate complex concepts and results effectively to both technical and non-technical stakeholders.

Required Skills & Qualifications

• Technical Expertise:
  • Strong proficiency in Python around Agentic, GenAI and framework such as LangChain, LangGraph,  CrewAI, Langfuse etc.
  • Hands-on experience with AWS ML ecosystem (Bedrock, SageMaker, Lambda, API Gateway).
  • Deep understanding of AI Agents, Agentic architecture, Generative AI, LLMs, NLP, and Conversational AI.
  • Deep understanding of Prompt engineering and Prompt management, refining and optimizing prompts to enhance the outcomes of Large Language Models (LLMs)
  • Experience with Database technologies like SQL, NoSQL and vector databases (e.g., DynamoDB, PGVector, CromaDB, FAISS etc.)
  • Proficiency with developing solutions on cloud platform preferably AWS platform
  • Good Engineering experience with large-scale AI/ML systems.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.