Lead Software Engineer - Ai Engineer

JPMorgan Chase & Co.; Plano (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. in Plano, TX seeks a Lead Software Engineer to architect, design, and deliver scalable cloud-native microservices and AI-enabled solutions. You will own key technical decisions, drive engineering best practices, and ensure secure, production-ready systems.

The role emphasizes hands-on Java development, AWS-based infrastructure, and integration of LLM-driven capabilities with business workflows in a fast-paced Agile environment.

Qualifications

• Formal training or certification in Software Engineering and 5+ years applied experience.
• Strong system design, application development, and operational stability skills in production environments.
• Hands-on experience using enterprise-authorized AI-assisted software development tools with demonstrated ability to evaluate, validate, and refine AI outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
• Influence product design, application functionality, and technical operations by proposing pragmatic architectures, tradeoffs, and standards.
• Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration).
• Familiarity with AI/ML frameworks and ecosystems such as PyTorch, TensorFlow, scikit-learn, Hugging Face.
• Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).
• Expertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).
• Demonstrated experience leading effective use of approved AI-assisted software development tools with the ability to validate AI outputs for correctness, performance, and security.
• Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh).

Responsibilities

• Design and develop creative full-stack software solutions using innovative approaches.
• Lead the creation and implementation of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes; establishes validation standards and promotes reuse of patterns.
• Leads usage of enterprise AI coding assist tools to enhance quality and delivery timelines with peer review and automated tests.
• Applies knowledge of SDLC tools, including AI-assisted development and automation, to improve value realized by automation.
• Architect and deliver cloud-native microservices and APIs (REST/streaming) with security and scalability in mind.
• Identify and automate recurring operational issues to improve stability and observability.
• Communicate project status and manage priorities across multiple initiatives.
• Collaborate within a Scrum team, participate in Agile ceremonies, and support a diverse and inclusive culture.
• Code in Java, AWS ECS, EKS, and Postgres.
• Utilize AI agents to improve quality and delivery timelines.

Skills

Software Engineering

System design

Operational stability

AI-assisted development

Responsible AI

Cloud platforms

Java

Python

RESTful APIs

Tools

AWS ECS

EKS

Postgres

Docker

Kubernetes

Helm

CI/CD

PyTorch

TensorFlow

Job description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, Automation & AI Solutions team, you will architect, design, and deliver scalable software products that combine cloud-native microservices, and generative AI/LLM capabilities. You will own key technical decisions, drive engineering best practices, and ensure solutions are secure, reliable, and production-ready.

Job responsibilities:

• Design and develop creative full-stack software solutions using innovative approaches.
• Lead the creation and implementation of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.
• 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.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• 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.
• Architect and deliver cloud-native microservices and APIs (REST/streaming), ensuring scalability, resilience, and strong security controls.
• Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).
• Communicate project status clearly and manage priorities across multiple initiatives.
• Collaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, opportunity, and inclusion.
• Codes in Java, AWS ECS, EKS, and Postgres
• Utilizes AI agents (CoPilot, Claude) to improve quality and delivery timelines

Required qualifications, capabilities, and skills

• Formal training or certification in Software Engineering and 5+ years applied experience
• Strong system design, application development, and operational stability skills in production environments.
• Hands‑on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; a ability to guide peers on safe and effective usage within team practices.
• Influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
• Hands‑on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration, evaluation/guardrails).
• Familiarity with AI/ML frameworks and ecosystems such as PyTorch, TensorFlow, scikit‑learn, Hugging Face.
• Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).
• Expertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).
• 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.
• Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).

Preferred qualifications, capabilities, and skills:

• Cloud certification in AWS, GCP, or Azure.
• Working knowledge of Python (for AI/ML integrations) a plus
• Experience with multi region service deployments and zero downtime deployment
• Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs
• Strong communication skills and a proactive approach to continuous improvement.
• Track record delivering scalable, reliable, and secure products from concept to launch.
• Advanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a plus.