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- Tasks: Lead the deployment of cutting-edge AI systems and collaborate with talented teams.
- Company: Join a forward-thinking team in a leading Commercial & Investment Bank.
- Benefits: Career growth, hands-on learning, and impactful AI projects.
- Other info: Collaborate with experts and make a meaningful impact in a dynamic environment.
- Why this job: Shape the future of Generative AI and drive innovation in financial services.
- Qualifications: Experience in AI, machine learning, and multi-agent systems required.
The predicted salary is between 100000 - 150000 £ per year.
Join us to shape the future of Generative AI in the Commercial & Investment Bank. You will have the opportunity to work with cutting-edge technology and collaborate with talented teams across multiple business lines. Your expertise will help us deliver innovative agentic solutions that automate complex workflows and drive business impact. We value your technical depth, creativity, and ability to translate ideas into operational excellence. Be part of a team that is leading the way in AI adoption and transformation.
As an Agent Specialist Lead in the GenAI Enablement Team within the Chief Analytics Office, you will advise and guide teams in designing, implementing, and scaling agentic systems that extend our Generative AI capabilities. You will work closely with stakeholders, product managers, data scientists, and engineering partners to operationalize LLM-powered agents that automate reasoning, retrieval, and workflow execution across the Commercial & Investment Bank. Your role will be central to driving the adoption of GenAI tools and frameworks, including our proprietary LLM Suite platform. You will help foster a collaborative and innovative team culture focused on delivering impactful AI solutions.
Job Responsibilities:
- Lead the deployment, and support of LLM-powered agentic systems, ensuring scalability, observability, and high performance in production environments.
- Collaborate with business stakeholders to identify high-impact use cases and translate business requirements into agentic architectures integrated with existing data and workflow platforms.
- Provide technical guidance to cross-functional teams, fostering hands-on learning and effective collaboration across research, engineering, and product functions.
- Enhance agent orchestration, retrieval, and reasoning capabilities in partnership with engineering teams to improve performance, reliability, and resilience at scale.
- Work with AI researchers, ML engineers, and developers to advance agentic design, including dynamic planning, tool use, and multi-agent collaboration.
- Troubleshoot and optimize deployments, ensuring smooth implementation, compliance with risk controls, and continuous performance improvement.
Required Qualifications, Capabilities, and Skills:
- Significant experience in AI, machine learning, or intelligent systems, with recent experience building or deploying LLM-powered or agentic solutions.
- Hands-on experience designing and implementing multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI, with practical understanding of LLM orchestration, retrieval augmentation (RAG), tool calling, and dynamic reasoning.
- Experience integrating agentic systems into enterprise data and workflow environments, ensuring robustness, and maintainability.
- Proficiency in Python, with experience extending orchestration components and building APIs or tool interfaces.
- Experience deploying AI systems on cloud platforms using Docker, Kubernetes, and microservices integration.
- Experience deploying and optimising GenAI and LLM-based systems, including performance evaluation and monitoring.
- Strong analytical foundation with the ability to reason about system performance, model behaviour, and control trade-offs.
- Proven ability to influence and align cross-functional teams through collaboration.
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering or a related technical field required.
- Strong communication skills for technical and non-technical audiences.
Preferred Qualifications, Capabilities, and Skills:
- Familiarity with risk controls and compliance in AI deployments.
- Ability to drive innovation in agentic system design and orchestration.
- Experience in financial services or enterprise environments.
- Track record of mentoring and developing talent in AI teams.
Why Join Us?
You will be part of a forward-thinking team driving the GenAI strategy for the Commercial & Investment Bank. We offer opportunities for career growth, hands-on learning, and the chance to make a meaningful impact through innovative AI solutions. Collaborate with experts, work on industry-leading projects, and help shape the future of AI at our firm.
Agent Specialist Lead - Vice President - GenAI Enablement in London employer: JPMorganChase
Contact Detail:
JPMorganChase Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Agent Specialist Lead - Vice President - GenAI Enablement in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working in AI or financial services. Attend meetups, webinars, and conferences to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to LLM-powered systems or agentic solutions. This will give potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with Python, cloud platforms, and multi-agent systems. Practice explaining complex concepts in simple terms!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our innovative team.
We think you need these skills to ace Agent Specialist Lead - Vice President - GenAI Enablement in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AI and LLM-powered solutions. We want to see how your skills align with the role, so don’t hold back on showcasing your technical depth and creativity!
Showcase Your Collaboration Skills: Since this role involves working closely with various teams, it’s essential to demonstrate your ability to collaborate effectively. Share examples of past projects where you’ve worked with cross-functional teams to deliver impactful results.
Highlight Relevant Experience: Be specific about your hands-on experience with agentic systems and frameworks like LangChain or AutoGen. We’re looking for candidates who can hit the ground running, so make sure to detail your relevant projects and achievements.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity in shaping the future of Generative AI!
How to prepare for a job interview at JPMorganChase
✨Know Your Tech Inside Out
Make sure you’re well-versed in the latest AI and machine learning technologies, especially those related to LLM-powered systems. Brush up on frameworks like LangChain and AutoGen, and be ready to discuss how you've applied them in real-world scenarios.
✨Showcase Your Collaboration Skills
Since this role involves working with cross-functional teams, prepare examples that highlight your ability to collaborate effectively. Think of times when you’ve influenced stakeholders or worked closely with engineers and product managers to achieve a common goal.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during your interview. Be ready to explain your experience with Python, Docker, and Kubernetes, and how you’ve optimised AI systems in production environments. Practice articulating complex concepts in a way that’s easy to understand.
✨Demonstrate Your Problem-Solving Skills
Be prepared to tackle hypothetical scenarios or case studies related to agentic systems. Show how you approach troubleshooting and optimisation challenges, and share specific examples of how you’ve improved system performance or reliability in past projects.