Lead Software Engineer – AIML Data Platform (Data, Python, Containers/Kubernetes)

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the AI and Machine Learning Data Platforms team, you are an integral part of an agile team that works to enhance, build, and deliver trusted, market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job Responsibilities

• Executes creative software solutions across batch, streaming, ML, and LLM workloads, spanning design, development, and technical troubleshooting, with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems while trading off latency, throughput, resilience, cost, and governance.

• Develops secure and high-quality production code, and reviews and debugs code written by others; leads design and code reviews, tunes performance, and owns incident response under pressure.

• Engineers high-volume data platforms, covering ingestion, transformation, enrichment, storage, and consumption, on distributed engines such as Spark, Ray, and Kafka, over lakehouse and search technologies like Iceberg and OpenSearch.

• Productionizes ML across repeatable training, evaluation, deployment, monitoring, and lifecycle management, taking models from notebook to reliable service with clear signals when quality drifts.

• Builds LLM systems that hold up in production, including retrieval, tool use, orchestration, caching, batching, evaluation harnesses, and human-in-the-loop controls, with genuine attention to accuracy, latency, spend, safety, and auditability.

• Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation; sound delivery automation and infrastructure-as-code are simply expected.

• Champions responsible AI use by setting expectations for validating AI outputs for correctness, performance, and security, with attention to data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security, and coaches engineers on safe, compliant adoption.