Lead Software Engineer
About The Position
As a Lead Software Engineer at JPMorgan Chase within the Corporate Technology Data Strategy & Architecture organization, you will work as part of an agile team to enhance, build, and deliver secure, stable, scalable technology products. You will use deep technical expertise and problem-solving skills to address challenges across multiple technologies and applications, driving business impact through your contributions. The role leads the design and delivery of complex software features and develops production AI systems, including agentic workflows and LLM-based applications. It also involves owning production AI observability, evaluation, and safety; improving operational stability; mentoring engineers; and contributing to firmwide engineering frameworks and Software Development Life Cycle practices. The lead also promotes governed, measurable adoption of approved AI-assisted engineering tools and practices. JPMorgan Chase offers a competitive total rewards package, with base salary determined by role, experience, skill set, and location. Eligible roles may also receive commission-based pay or discretionary incentive compensation. Benefits and programs are available based on eligibility; additional compensation and benefit details are provided during the hiring process.
Requirements
- Formal training or certification in software engineering concepts and at least 5 years of applied experience.
- Hands-on experience building and shipping LLM-based applications and agentic systems with tool use, memory, and multi-step reasoning in production environments.
- Advanced proficiency in one or more programming languages, particularly Python and/or Java.
- Deep experience with large-scale data processing, microservices, API design, and event streaming such as Kafka.
- Working knowledge of relational and NoSQL databases, vector stores, and data lake architectures.
- Experience with caching technologies such as Redis or MemCached; observability tools such as Dynatrace, Splunk, or Grafana; and orchestration frameworks such as Airflow or Temporal.
- Proficiency in CI/CD, test-driven development, automation, and all aspects of the Software Development Lifecycle.
- Strong understanding of agile methodologies, application resiliency, and security best practices.
- Practical cloud-native engineering experience with AWS, Azure, or GCP.
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools at work, 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, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching senior engineers or leads on compliant usage patterns and controls.
Nice To Haves
- Experience with LLM orchestration frameworks.
- Hands-on experience with model-serving infrastructure or managed endpoints such as AWS Bedrock or Azure OpenAI.
- Familiarity with AI evaluation and observability practices, including evaluation frameworks, red-teaming, prompt drift detection, and cost and latency monitoring.
- Understanding of agentic design patterns and how to constrain agent autonomy in high-stakes financial workflows.
- Experience with modern data platforms such as Databricks or Snowflake.
- Hands-on experience with Spark/PySpark and big data processing at scale.
- Knowledge of the financial services industry and its technology systems.
- Awareness of AI risk and regulatory considerations relevant to AI use in financial decision-making.
Responsibilities
- Develop secure, high-quality production code for data-intensive applications and platforms; review and debug code written by others.
- Lead end-to-end design and implementation of complex software features, from requirements through deployment and operational stability.
- Drive technical decisions affecting application design, functionality, performance, and reliability.
- Build and maintain agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls suited to regulated financial services environments.
- Implement LLM-based applications, including RAG pipelines, embedding workflows, vector store integrations, and model-serving infrastructure.
- Own observability, evaluation, and safety of production AI systems, including prompt monitoring, output validation, cost tracking, and latency optimization.
- Identify and execute opportunities to automate remediation of recurring issues and improve operational stability.
- Develop creative software solutions through design, development, and technical troubleshooting of complex and ambiguous problems.
- Mentor and coach junior and mid-level engineers, conduct code reviews, and share engineering best practices.
- Contribute to firmwide frameworks, tools, and SDLC practices as an engaged member of the engineering community.
- Drive adoption and governance of approved AI-assisted engineering practices across teams, establish measurable validation standards, and promote reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Use tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized from automation at scale.
Benefits
- Comprehensive health care coverage
- On-site health and wellness centers
- Retirement savings plan
- Backup childcare
- Tuition reimbursement
- Mental health support
- Financial coaching
- Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
