Location: LONDON, LONDON, United Kingdom

Out of the successful launch of Chase in 2021, were a new team, with a new mission. Were 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. Were people-first. We value collaboration, curiosity and commitment.

As a Principal Software Engineer - Engineering Manager at JPMorganChase within the Accelerator Business, you would be responsible for the vision for the Data and AI capabilities of the platform - to enable product teams to focus on their core problems and platform would cover the rest. To give a not complete list of examples: data ingestion, transformation, exposing in various query engines, LLM patterns, audit, safety guardrails, compliance and observability.

While were looking for professional skills, culture is just as important to us. We understand that everyone's unique and that diversity of thought, experience and background is what makes a good team, great. By bringing people with different points of view together, we can represent everyone and truly reflect the communities we serve. This way, there's scope for you to make a huge difference on us as a company, and on our clients and business partners around the world.

Technologies we use: Java, Kotlin, Kubernetes, Apache Kafka, GCP, BigQuery, Spark, VertexAI, ModelArmor, DeepEval, Google ADK.

Job responsibilities:

• Set the vision and multi-year strategy for the Data & AI Platform that powers Chases next-generation digital experiences, translating enterprise priorities into an executable roadmap and measurable outcomes.
• Lead and scale a multi-discipline organization spanning data platform engineering and AI/MLOps, establishing clear ownership, org structure, operating rhythms, and standards for delivery.
• Own the platforms end-to-end data foundationingestion, transformation, orchestration, metadata/catalog, quality, and governed data productsbuilt for reliability, scalability, and self-service adoption.
• Serve as the executive steward for compliant use of customer data, ensuring privacy, access controls, lineage, retention, and auditability are embedded by design and aligned to firm risk and regulatory expectations.
• Define and deliver platform enablement for LLM-powered applications, including reference architectures, developer tooling, model onboarding and deployment patterns, evaluation and testing, observability, and cost/latency guardrails.
• Establish engineering excellence and operational maturity through SLOs, resiliency practices, incident management, release governance, capacity planning, and continuous improvement across the platform.
• Drive a product-oriented platform model by partnering with product, security, legal, risk, architecture, and engineering leaders to prioritize the highest-leverage capabilities and accelerate adoption across teams.
• Enable data-driven product development at scale through trusted analytics pipelines, standardized telemetry, experimentation support, and consistent metrics to inform decisions and improve customer outcomes.
• Attract, develop, and retain top talent by building leadership depth, setting high standards, coaching managers and senior engineers, and fostering a culture of ownership, inclusion, and accountability.

Required qualifications, capabilities and skills

• Being a problem solver: you can independently analyze a problem and come up with options on how to solve it.
• Flexibility regarding tools and languages: for example you have to be open to debug an permission issue one day in a python service and dig into some Java/Kotlin out-of-memory issue the other day (of course we take into account your expertise and you will have team members to help you out!).
• Knowledge of data structures.
• Experience with either Kubernetes or Docker.
• Experience with cloud technologies (AWS/Azure/GCP) and distributed systems, web technologies and event drive architectures.
• Experience in leading people.

Preferred qualifications, capabilities and skills

• Experience with message brokers (Kafka, RabbitMQ, Pulsar etc.).
• Experience with Kafka Connect.
• Preferably experience in setting up data platforms, setting standards - not just pipelines.
• Preferably experience in a distributed data processing environment/framework (e.g. Spark or Flink).
• Familiarity with advanced AI/ML concepts and protocols, such as Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP)
• Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow)
• Experience with a deploying to a GenAI platform a production system: Google VertexAI, OpenAI, AWS Bedrock, LangChain, etc.

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