Applied AI Engineering Lead - VP, Markets Operations
JPMorgan Chase · London, United Kingdom · 1mo ago
Join us at the forefront of applied AI innovation and help build the next generation of agentic AI applications at one of the world’s 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 cases
- Define and drive AI engineering architecture patterns for scalable, secure, reusable, and production-ready AI, machine learning, and generative AI solutions
- Design and implement agent harnesses, orchestration layers, tool-use frameworks, workflow automation patterns, and guardrails for enterprise AI applications
- Develop context management strategies, including retrieval approaches, memory patterns, prompt and context construction, grounding, data access controls, and lifecycle management of contextual information
- Build and enhance robust AI services and infrastructure using modern engineering practices, including APIs, event-driven patterns, CI/CD, Infrastructure-as-Code, observability, and automated testing
- Partner with AI researchers, data scientists, and software engineers to translate emerging AI capabilities into practical, reliable, and compliant enterprise applications
- Establish evaluation, monitoring, and feedback mechanisms for AI systems, including quality measurement, hallucination reduction, regression testing, model performance tracking, and operational risk controls
- Design approaches for continual learning and improvement, including human-in-the-loop feedback, telemetry-driven enhancement, model, prompt, and version management, and safe release practices
- Collaborate with Markets Operations stakeholders to understand process pain points and translate them into AI-enabled technology solutions with measurable business impact
- Document and communicate architecture decisions, design tradeoffs, engineering standards, and implementation patterns to technical and non-technical audiences
- Mentor engineers and contribute to a culture of technical excellence, innovation, responsible AI adoption, and continuous learning
Required Qualifications, Capabilities, and Skills
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or related field, or equivalent practical experience
- Strong software engineering experience with Python and experience designing, building, and operating production-grade applications
- Experience designing and building AI, machine learning, generative AI, or agentic applications, including integration with enterprise systems and workflows
- Strong understanding of LLM application patterns, including prompt engineering, retrieval-augmented generation, tool calling, context management, evaluation, and guardrails
- Experience with RESTful API design, development, and integration, including frameworks such as FastAPI
- Experience with data engineering concepts, ETL and data pipelines, structured and unstructured data, and integration with enterprise data platforms
- Experience with CI/CD, automated testing, observability, production monitoring, and operational readiness practices
- Familiarity with Infrastructure-as-Code solutions such as Terraform and cloud or container-based deployment patterns
- Working knowledge of database design and integration, including relational, document, vector, or graph-based data stores
- Understanding of security, controls, compliance, and model risk considerations relevant to enterprise AI systems
- Strong verbal and written communication skills, including the ability to influence architecture decisions and work effectively across multidisciplinary teams
Preferred Qualifications, Capabilities, and Skills
- Experience designing or operating multi-agent systems, agent orchestration frameworks, workflow automation platforms, or tool-augmented LLM applications
- Experience with context engineering techniques, including retrieval strategies, embeddings, vector databases, knowledge graphs, semantic search, memory management, and grounding approaches
- Experience building evaluation frameworks for AI applications, including golden datasets, automated scoring, human review workflows, red teaming, regression testing, and production quality monitoring
- Experience with continual learning or continuous improvement patterns for AI systems, including feedback loops, telemetry analysis, prompt and model versioning, and experimentation frameworks
- Familiarity with Markets Operations processes, trade lifecycle, post-trade operations, reconciliations, controls, exception management, or operational risk
- Experience applying Artificial Intelligence in finance, markets, operations, risk, or large-scale enterprise technology environments
- Strong presentation, stakeholder partnership, technical leadership, and project execution skills
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