Sr Lead Software Engineer

JPMorgan Chase & Co. • Houston, TX

3d

About The Position

As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Technology Data Strategy & Architecture organization, you will work as part of an agile team to build and enhance secure, stable, scalable technology products. You will apply deep technical expertise and problem-solving skills to challenges spanning multiple technologies and applications, driving business impact through the design, development, and delivery of production software and AI systems.

The role leads end-to-end implementation of complex software features and contributes to technical decisions affecting application design, functionality, performance, and reliability. It includes building production LLM applications and agentic AI systems, with appropriate tool integrations, human-in-the-loop controls, observability, evaluation, and safety for regulated financial services environments. You will also help advance approved AI-assisted engineering practices and automation across teams, while mentoring engineers and contributing to firmwide engineering frameworks and SDLC practices.

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 in the work environment, 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 methods for constraining 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 that influence 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 for 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.
• Design, develop, and troubleshoot creative software solutions to address 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.
• Apply knowledge of Software Development Life Cycle toolchain capabilities, including approved AI-assisted development and automation, to improve the value realized by automation at scale.

Benefits

• Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed