**Applied AI Engineering Lead - VP, Markets Operations**: Lead design and implementation of agentic AI apps enhancing Markets Operations. Bridge AI capabilities with enterprise engineering for measurable impact. **Responsibilities** include: designing AI app architecture, establishing AI engineering patterns, implementing AI services and infrastructure, and partnering with AI researchers, software engineers, and operations stakeholders. **Requirements**: Bachelor's/Master's degree in Computer Science/related field, strong software engineering experience (Python), AI application design, API integration, data engineering, and security understanding. **Preferred skills**: experience with multi-agent systems, context engineering, evaluation frameworks, and continual learning.
Full Job Description
Location: LONDON, United Kingdom
Join us at the forefront of applied AI innovation and help build the next generation of agentic AI applications at one of the worlds largest banks. You will bridge cutting-edge AI capabilities with enterprise-grade engineering to deliver measurable impact across Markets Operations. You will collaborate with engineers, researchers, data scientists, and business leaders in a hands-on, builder-focused environment. You will have the opportunity to grow your career while helping advance safe, reliable, and effective AI in financial services.
As an Applied AI Engineering Lead - Vice President in Markets Operations, you will lead the design and implementation of agentic AI applications that improve operational workflows, controls, productivity, and engineering practices. You will build reusable AI engineering patterns, context management frameworks, evaluation pipelines, and production-ready AI services. You will partner closely with software engineers, AI and data science specialists, and operations stakeholders to identify high-value opportunities and deliver robust solutions integrated with strategic platforms and operational processes.
Job Responsibilities
Lead the design, development, and implementation of agentic AI applications that support Markets Operations workflows, controls, exception management, and productivity use casesDefine and drive AI engineering architecture patterns for scalable, secure, reusable, and production-ready AI, machine learning, and generative AI solutionsDesign and implement agent harnesses, orchestration layers, tool-use frameworks, workflow automation patterns, and guardrails for enterprise AI applicationsDevelop context management strategies, including retrieval approaches, memory patterns, prompt and context construction, grounding, data access controls, and lifecycle management of contextual informationBuild and enhance robust AI services and infrastructure using modern engineering practices, including APIs, event-driven patterns, CI/CD, Infrastructure-as-Code, observability, and automated testingPartner with AI researchers, data scientists, and software engineers to translate emerging AI capabilities into practical, reliable, and compliant enterprise applicationsEstablish evaluation, monitoring, and feedback mechanisms for AI systems, including quality measurement, hallucination reduction, regression testing, model performance tracking, and operational risk controlsDesign approaches for continual learning and improvement, including human-in-the-loop feedback, telemetry-driven enhancement, model, prompt, and version management, and safe release practicesCollaborate with Markets Operations stakeholders to understand process pain points and translate them into AI-enabled technology solutions with measurable business impactDocument and communicate architecture decisions, design tradeoffs, engineering standards, and implementation patterns to technical and non-technical audiencesMentor engineers and contribute to a culture of technical excellence, innovation, responsible AI adoption, and continuous learningRequired Qualifications, Capabilities, and Skills
Bachelors or Masters degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or related field, or equivalent practical experienceStrong software engineering experience with Python and experience designing, building, and operating production-grade applicationsExperience designing and building AI, machine learning, generative AI, or agentic applications, including integration with enterprise systems and workflowsStrong understanding of LLM application patterns, including prompt engineering, retrieval-augmented generation, tool calling, context management, evaluation, and guardrailsExperience with RESTful API design, development, and integration, including frameworks such as FastAPIExperience with data engineering concepts, ETL and data pipelines, structured and unstructured data, and integration with enterprise data platformsExperience with CI/CD, automated testing, observability, production monitoring, and operational readiness practicesFamiliarity with Infrastructure-as-Code solutions such as Terraform and cloud or container-based deployment patternsWorking knowledge of database design and integration, including relational, document, vector, or graph-based data storesUnderstanding of security, controls, compliance, and model risk considerations relevant to enterprise AI systemsStrong verbal and written communication skills, including the ability to influence architecture decisions and work effectively across multidisciplinary teamsPreferred Qualifications, Capabilities, and Skills
Experience designing or operating multi-agent systems, agent orchestration frameworks, workflow automation platforms, or tool-augmented LLM applicationsExperience with context engineering techniques, including retrieval strategies, embeddings, vector databases, knowledge graphs, semantic search, memory management, and grounding approachesExperience building evaluation frameworks for AI applications, including golden datasets, automated scoring, human review workflows, red teaming, regression testing, and production quality monitoringExperience with continual learning or continuous improvement patterns for AI systems, including feedback loops, telemetry analysis, prompt and model versioning, and experimentation frameworksFamiliarity with Markets Operations processes, trade lifecycle, post-trade operations, reconciliations, controls, exception management, or operational riskExperience applying Artificial Intelligence in finance, markets, operations, risk, or large-scale enterprise technology environmentsStrong presentation, stakeholder partnership, technical leadership, and project execution skills
#CIBAppliedAI
Design and build agentic AI applications for Markets Operations, including context management, evaluation, and continual learning.
