Location
London

Hours
Full Time

Salary
Competitive, commensurate with experience

About the Role
Out of the successful launch of Chase in 2021, we're a new team with a new mission. We're creating products that solve real world problems and put customers at the center - all in an environment that nurtures skills and helps you realize your potential. Our team is key to our success. We're people-first, valuing collaboration, curiosity and commitment.

As a Principal Software Engineer - Applied AI ML Director within JPMorganChase's Accelerator Business, you will be at the heart of this venture, focused on delivering smart ideas to our customers. You have a curious mindset, thrive in collaborative squads, and are passionate about new technology. You are solution-oriented, commercially savvy, and have a strong fintech acumen.

You will work within tribes and squads focused on specific products and projects, with opportunities to move between them depending on your strengths and interests. We value culture as much as professional skills, embracing diversity of thought, experience, and background to build great teams that truly reflect the communities we serve. This role offers the scope to make a significant impact on the company, clients, and business partners worldwide.

Key Responsibilities:
- Design and develop scalable, self-service solutions for documentation, SDKs, configurations, and pipelines to enable rapid deployment of GenAI applications including Retrieval-Augmented Generation (RAG) pipelines and agents with planning, memory, and workflow orchestration.
- Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management.
- Develop systems to monitor model performance and address data and model drift.
- Recommend best practices for model integration and deployment patterns.
- Design and implement effective testing strategies including unit, component, integration, end-to-end, performance, and champion/challenger tests; establish output validation best practices and guardrails to reduce hallucinations.
- Ensure platform compliance with data privacy, security, and regulatory standards.
- Mentor team members on platform design principles and best practices.
- Guide colleagues on coding practices, design principles, and implementation patterns for high-quality, maintainable solutions.
- Deploy scalable AI services to cloud infrastructure, ensuring monitoring and observability for agent performance.
- Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics, and feedback loops to continuously improve reliability and utility.

Experience
- Proficiency in Java and/or Python programming languages.
- Experience deploying production systems to GenAI platforms such as Google VertexAI, OpenAI, AWS Bedrock, or LangChain.
- Utilized cloud technologies (AWS, Azure, GCP), distributed systems, CI/CD tools, infrastructure-as-code tools, and containerization/orchestration tools (Docker, Kubernetes) to operate, support, and secure mission-critical applications.
- Previous experience deploying and managing LLM-model based applications and agents.
- Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets.
- Familiarity with cloud-native microservices architecture.
- Hands-on experience with advanced AI/ML concepts and protocols including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP).
- Practical experience with agentic frameworks such as LangChain, CrewAI, AutoGen, LangGraph, ADK.
- Strong communication skills for both technical and non-technical audiences.

About You
- Solution-oriented and commercially savvy with a passion for fintech.
- Collaborative team player who thrives in agile squads and cross-functional tribes.
- Curious and committed to continuous learning and innovation.
- Embraces diversity and contributes to an inclusive culture.
- Comfortable mentoring and guiding colleagues on best practices and design principles.

Qualifications
- Preferred experience working in highly regulated environments or industries.
- Experience with distributed computing, data sharding, and performance optimization.
- Demonstrated experience in financial services, particularly retail banking operations.