We have an exciting and rewarding opportunity for you to take your data engineering career to the next level.
As a Lead Software Engineer - Databricks/PySpark/AI at JPMorganChase within the Corporate Sector-Global Finance team, you will serve as a senior hands-on developer and technical leader within an agile team, responsible for building, delivering, and optimizing cutting-edge data products that power agentic AI systems — autonomous AI agents capable of planning, reasoning, and executing multi-step tasks. In this role, you will write production-quality code daily, drive implementation of essential technology solutions including data infrastructure, tool integrations, and retrieval systems that enable AI agents to access, interpret, and act on enterprise data in support of the firm’s business goals. You will be expected to mentor junior engineers, collaborate with cross-functional stakeholders, and champion engineering excellence through hands-on delivery.
Job Responsibilities
- Building and optimizing data pipelines and workflows that serve as the backbone for agentic AI systems, ensuring agents have reliable, real-time access to high-quality, structured and unstructured data
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment 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.
- Developing data retrieval and indexing layers that enable AI agents to autonomously search, query, and synthesize information across multiple data sources
- Building and maintaining tool-use infrastructure — APIs, data services, and function endpoints — that AI agents invoke to execute tasks, retrieve data, and interact with enterprise systems
- Implementing and enforcing best practices for data management, ensuring data quality, security, and compliance, including governance of data consumed and generated by autonomous AI agents
Hands-on development of secure, high-quality production code following AWS best practices, and deploying efficiently using CI/CD pipelines;
Building orchestration and state management layers that support multi-step agent workflows, including memory, context persistence, and task chaining
Writing and reviewing code daily, conducting thorough code reviews, and raising the technical bar across the team;
Mentoring and guiding junior and mid-level engineers through pairing, code reviews, and technical coaching
Collaborating with product owners, data scientists, and business stakeholders to translate business requirements into working, production-ready agentic AI solutions;
Evaluating and adopting emerging agentic AI frameworks, tools, and data engineering practices to continuously improve the team’s development capabilities
Required Qualifications, Capabilities, and Skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- 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
- Expert-level programming skills in Python/PySpark with a strong portfolio of production-grade code
Extensive hands-on experience with Databricks and the AWS cloud ecosystem, including AWS Glue, S3, SQS/SNS, Lambda,
Spark and SQL
- Strong hands-on experience with Lakehouse/Delta Lake architecture, application development, testing, and ensuring operational stability; Snowflake, Terraform and LLMs; Data Observability, Data Quality, Query Optimization & Cost Optimization
- In-depth knowledge of Big Data and data warehousing concepts at enterprise scale
- Extensive experience with CI/CD processes and automated testing frameworks
- Solid understanding of agile methodologies, including DevOps practices, application resiliency, and security measures
- Understanding of agentic AI concepts — how autonomous AI agents plan, reason, use tools, and execute multi-step workflows — and the data infrastructure required to support them
Experience building APIs, data services, and retrieval systems that serve as the connective tissue between AI agents and enterprise data
Preferred Qualifications, Capabilities, and Skills
- Experience with agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI, OpenAI Assistants API) and understanding of how data engineering underpins agent orchestration
- Familiarity with tool-use and function-calling patterns for LLM-based agents, including building and exposing APIs and data endpoints that agents can invoke autonomously
- Experience with vector databases (e.g., Pinecone, FAISS, Chroma) and embedding workflows for powering agent memory, semantic search, and retrieval-augmented generation (RAG)
- Exposure to agent memory and state management patterns — short-term context windows, long-term persistent memory stores, and conversation/task history management
- Familiarity with guardrails and safety frameworks for autonomous AI systems, including input/output validation, action approval workflows, and human-in-the-loop controls
- Understanding of observability and monitoring for agentic systems — tracing agent decision paths, logging tool invocations, and debugging multi-step autonomous workflows
- Understanding of responsible AI principles, particularly around autonomous decision-making, data provenance, and auditability of agent actions
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
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.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
Skills Required
- Formal training or certification in software engineering concepts
- At least 5 years of applied software engineering experience
- Experience leading approved AI-assisted software development tools and validating AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI, data sensitivity, secure input and output handling, resiliency, and security expectations
- Expert-level Python and PySpark programming skills with production-grade code experience
- Extensive hands-on experience with Databricks and AWS, including AWS Glue, S3, SQS/SNS, Lambda, Spark, and SQL
- Hands-on experience with Lakehouse and Delta Lake architecture, application development, testing, and operational stability
- Experience with Snowflake, Terraform, LLMs, data observability, data quality, query optimization, and cost optimization
- In-depth knowledge of enterprise-scale big data and data warehousing concepts
- Extensive experience with CI/CD processes and automated testing frameworks
- Understanding of agile methodologies, DevOps practices, application resiliency, and security measures
- Understanding of agentic AI concepts and supporting data infrastructure
- Experience building APIs, data services, and retrieval systems for AI agents and enterprise data
- Experience with agentic AI frameworks such as LangGraph, AutoGen, CrewAI, or OpenAI Assistants API
- Familiarity with tool-use and function-calling patterns for LLM-based agents
- Experience with vector databases and embedding workflows for memory, semantic search, and RAG
- Exposure to agent memory and state-management patterns
- Familiarity with guardrails, safety frameworks, action approval workflows, and human-in-the-loop controls
- Understanding of observability and monitoring for agentic systems
- Understanding of responsible AI principles, data provenance, and auditability of agent actions
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 — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
JPMorganChase Insights
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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