JPMorganChase
Jobs
Lead Software Engineer – AIML Data Platform (Data, Python, Containers/Kubernetes)
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The Role
Leads development of secure, scalable data platforms supporting batch, streaming, ML, and LLM workloads. Designs distributed data systems using Spark, Ray, Kafka, Iceberg, and OpenSearch; productionizes model training, deployment, monitoring, and lifecycle management; and builds governed LLM solutions. Provides technical leadership, code review, incident response, vendor evaluation, responsible AI guidance, automation, and mentorship across engineering teams.
Summary Generated by Built In
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
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.
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
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Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Provides technical leadership on data projects across engineers, product managers, and stakeholders, and provides mentorship and guidance to junior engineers, leaving systems others can confidently own.Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience.
- Hands-on practical experience delivering system design, application development, testing, and operational stability.
- Demonstrable strength in a cloud-based, cloud-native engineering environment.
- Advanced Python, with additional programming languages such as Java, Kotlin, or Scala a plus, and the range to work across more than one.
- Deep distributed-systems fundamentals: partitioning, parallelism, state, fault tolerance, and performance tuning under real load.
- Production experience across data engineering and streaming platforms at meaningful volume.
- Practical ML engineering across inference, evaluation, monitoring, and deployment, not just model training.
- Real LLM delivery beyond prototypes, with a clear view of quality, cost, safety, and governance.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting), with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
- Proficient in all aspects of the Software Development Life Cycle, with a good understanding of databases and data structures.
- Advanced understanding of agile methodologies such as CI/CD, application resiliency, and security.
- A proven record of leading technical delivery across teams while contributing directly to the code, with clear, pragmatic, evidence-driven communication.
- In-depth knowledge of the financial services industry and its IT systems, and practical cloud-native experience; regulated or high-governance environments, agent frameworks, MCP, RAG, and vector search, Kubernetes and modern observability, and real-time analytics or search-centric architectures are a plus.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Skills Required
- Formal training or certification in software engineering concepts
- Advanced hands-on experience with system design, application development, testing, and operational stability
- Experience in cloud-based or cloud-native engineering environments
- Advanced Python programming skills
- Experience working across multiple programming languages
- Deep understanding of distributed-systems fundamentals, including partitioning, parallelism, state, fault tolerance, and performance tuning
- Production experience with data engineering and streaming platforms at meaningful volume
- Practical ML engineering experience covering inference, evaluation, monitoring, and deployment
- Real-world LLM delivery experience beyond prototypes, including quality, cost, safety, and governance
- Experience leading effective use of approved AI-assisted software development tools
- Understanding of responsible AI use, data sensitivity, secure input/output handling, resiliency, and security
- Experience coaching engineers on safe and compliant AI-assisted development
- Proficiency across the Software Development Life Cycle
- Understanding of databases and data structures
- Advanced understanding of agile methodologies, CI/CD, application resiliency, and security
- Proven record of leading technical delivery across teams while contributing directly to code
- Clear, pragmatic, evidence-driven communication skills
- Additional programming languages such as Java, Kotlin, or Scala
- Experience in financial services IT systems and regulated or high-governance environments
- Experience with agent frameworks, MCP, RAG, vector search, Kubernetes, modern observability, and real-time analytics or search-centric architectures
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Health coverage is considered comprehensive, including medical, dental, and vision, alongside wellness and mental health resources. Some locations add onsite health centers and related wellbeing support.
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Retirement Support — Retirement offerings include a 401(k)-type savings plan and related financial benefits, with options such as employee stock purchase participation. Financial planning resources are also highlighted to support long-term savings.
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Parental & Family Support — Paid parental leave of 16 weeks for birth or adoption is available for all parents. Child care and back-up child care resources further reinforce family support.
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The Company
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
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