Senior Lead Software Engineer (AI & Data Platforms)

JPMorganChase; Jersey City (NJ); On-site; USD 150,000 - 230,000; Full time

Job summary

JPMorgan Chase is seeking a Senior Lead Software Engineer within Corporate Technology Data Strategy & Architecture to drive secure, scalable data-driven software in a fast-paced agile team. You will own end-to-end design, build AI-enabled systems, and mentor engineers while aligning with firmwide SDLC and governance.

You will apply deep technical expertise to tackle data-intensive challenges across multiple technologies, ensuring secure handling, reliability, and performance in production

Qualifications

• Formal training or certification in software engineering with 5+ years of applied experience.
• Hands-on experience building LLM-based apps and agentic systems in production.
• Proficiency in Python and/or Java.
• Experience with large-scale data processing, microservices, API design, and Kafka.
• Knowledge of relational/NoSQL data stores, vector stores, and data lakes.
• Familiarity with caching, observability tools, and orchestration (Airflow/Temporal).
• CI/CD, TDD, automation and full SDLC discipline.
• Strong Agile, resiliency, and security practices.
• Cloud-native engineering experience (AWS/Azure/GCP).
• Experience leading AI-assisted dev tools adoption and governance.
• Understanding of responsible AI use and data handling.

Responsibilities

• Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and debugs code written by others.
• Leads end-to-end design and implementation of complex software features, from requirements through deployment and operational stability.
• Drives technical decisions that influence application design, functionality, performance, and reliability.
• Builds and maintains agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments.
• Implements LLM-based applications including RAG pipelines, embedding workflows, vector store integrations, and model serving infrastructure.
• Owns observability, evaluation, and safety of production AI systems — including prompt monitoring, output validation, cost tracking, and latency optimization.
• Identifies and executes opportunities to automate remediation of recurring issues and improve operational stability.
• Executes creative software solutions, including design, development, and technical troubleshooting to solve complex and ambiguous problems.
• Mentors and coaches junior and mid-level engineers, conducting code reviews and sharing engineering best practices.
• Contributes to firmwide frameworks, tools, and SDLC practices as an engaged member of the engineering community.
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

Skills

Python

Java

LLM-based apps

Data processing

API design

CI/CD

Cloud-native

Observability tools

Security best practices

AI governance

Tools

Kafka

Airflow

Temporal

Docker

Kubernetes

Databricks

Snowflake

Jenkins

Job description

JPMorgan Chase is seeking a Senior Lead Software Engineer within Corporate Technology Data Strategy & Architecture to drive secure, scalable data-driven software in a fast-paced agile team. You will own end-to-end design, build AI-enabled systems, and mentor engineers while aligning with firmwide SDLC and governance.

You will apply deep technical expertise to tackle data-intensive challenges across multiple technologies, ensuring secure handling, reliability, and performance in production