Lead Software Engineer - Databricks/PySpark/AI

JPMorgan Chase & Co.; Wilmington (NC); On-site; USD 140,000 - 190,000; Full time

Job summary

JPMorganChase is seeking a Lead Software Engineer to advance data engineering for agentic AI systems. You will work in the Corporate Sector-Global Finance team, delivering production-grade PySpark/Databricks dataproducts and mentoring engineers in a fast-paced agile setting.

You will champion secure, scalable data solutions, collaborate with data scientists and product owners, and drive best practices across the SDLC with a focus on reliability and performance.

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Experience leading AI-assisted software development tools and setting team expectations for AI output correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, data sensitivity, secure handling, resiliency and security.
• Expert-level programming skills in Python/PySpark with production-grade code.
• Extensive hands-on experience with Databricks and AWS, including Glue, S3, Lambda, Spark and SQL.
• Knowledge of Lakehouse/Delta Lake architectures, data observability, and cost optimization.
• In-depth knowledge of enterprise-scale data warehousing and CI/CD processes.
• Agile methodologies, DevOps practices, and security considerations.
• Understanding of agentic AI concepts and data infrastructure to support autonomous agents.
• Experience building APIs and data services for AI agents.

Responsibilities

• Build and optimize data pipelines and workflows for agentic AI systems with real-time access to high-quality data.
• Drive team adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operations, including automated testing and secure coding standards.
• Apply SDLC toolchain knowledge to improve automation and delivery value.
• Develop data retrieval and indexing layers enabling AI agents to search and synthesize information across data sources.
• Build and maintain APIs, data services, and endpoints for AI agents to interact with enterprise systems.
• Enforce data governance, quality, security, and compliance for data consumed and generated by autonomous AI agents.
• Hands-on production code development following AWS best practices and CI/CD pipelines; build orchestration and state management for multi-step workflows.
• Mentor junior engineers through pairing, reviews, and coaching; maintain engineering excellence.
• Collaborate with product owners, data scientists, and stakeholders to translate requirements into production-ready AI solutions; evaluate new AI frameworks and data engineering practices.

Skills

Python/PySpark

Databricks

AWS cloud

CI/CD

Big data

Agile/DevOps

Mentoring

Education

Bachelor's degree in CS or related field

Tools

Terraform

Snowflake

S3

Lambda

Job description

Job Description

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

About Us

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

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

About the Team

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.