Location
London

Hours
Full Time

Salary
Competitive salary and benefits package

About the Role
Join us to shape the future of AI-driven business operations. You will have the opportunity to create scalable, safe, and reliable agentic solutions that transform how we work. As an AI Engineer in our Agentic AI Solutions team, you will design, build, and deploy autonomous and assistive AI agents that streamline complex workflows. You will collaborate with cross-functional partners to translate operational needs into robust multi-agent systems, leveraging advanced frameworks and technologies. Your work will directly impact our business by delivering scalable and reliable AI solutions. We foster a culture of innovation, learning, and teamwork, where your contributions are valued and your growth is supported. Be part of a team that thrives on innovation and continuous improvement.

Job Responsibilities
Architect, develop, and productionize autonomous and assistive AI agents to enhance operations. Design multi-agent systems, including role definition, tool integration, planning, memory, and workflow orchestration using modern agent frameworks. Implement Retrieval-Augmented Generation (RAG) pipelines and semantic search with vector databases, including indexing, retrieval policies, and evaluation. Build and integrate agent tools and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing and error handling. Practice advanced prompt engineering and implement output validation and guardrails to reduce hallucinations. Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics, and feedback loops. Partner with stakeholders to define requirements, design intuitive human-AI interfaces, and deliver measurable business impact. Analyze data to inform agent capabilities, optimize retrieval, and drive data-driven decision-making; conduct A/B tests and performance evaluations. Mentor and guide team members on agent frameworks, LLM usage, safety, and best practices.

Why Join Us?
We offer a competitive salary and benefits package, opportunities for professional growth, and a collaborative, innovative environment focused on responsible AI. You will work on impactful projects that shape the future of finance and have the chance to make a real difference. J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals, and institutional investors. We value diversity and inclusion and are committed to providing reasonable accommodations for applicants and employees.

Experience
Proven experience building and deploying agentic AI applications in production environments. Expertise with ML frameworks such as PyTorch, TensorFlow, and scikit-learn. Hands-on experience with agent frameworks such as LangChain, CrewAI, AutoGen, LangGraph. Knowledge of generative models including transformers, GANs, VAEs, and diffusion models. Familiarity with cloud platforms and containerization technologies. Experience in financial services, especially investment banking operations, is preferred. Experience fine-tuning small language models with approaches like LoRA, QLoRA, DoRA; quantization and distillation is a plus. Familiarity with prompt optimization frameworks and building prompt pipelines and evaluation suites. Experience with distributed computing, data sharding, and performance optimization. Hands-on experience with AWS services related to AI deployment and workflow orchestration.

About You
Strong problem-solving skills and ability to work independently and collaboratively. Effective communication skills for technical and non-technical audiences. Passionate about innovation, continuous learning, and teamwork. Enthusiastic about building scalable, safe, and reliable AI solutions that deliver measurable business impact.

Qualifications
Advanced degree in Computer Science, Data Science, Machine Learning, or related field. Proficiency in Python, including writing comprehensive tests and building evaluation harnesses for agents and prompts. Understanding of data preprocessing, feature engineering, and model/agent evaluation techniques.