Sr Lead Software Engineer – Python, AWS, UI, Agentic AI (ADK/LangGraph)

📍 Job Overview

Job Title: Sr Lead Software Engineer – Python, AWS, UI, Agentic AI (ADK/LangGraph)

Company: JPMorgan Chase & Co.

Location: Plano, TX, United States

Job Type: Full time

Category: Software Engineering / Cloud Foundation Services

Date Posted: 2026-09-08T19:37:58

Experience Level: 5-10 years

Remote Status: On-site

🚀 Role Summary

• Drive architectural vision and end-to-end implementation of complex software solutions, encompassing system and component design, integration patterns, and cross-team technical decision-making.

• Own hands-on delivery of production AI/agentic applications utilizing ADK and LangGraph, including orchestration workflows, tool/function calling, and multi-step stateful graphs.

• Define and enforce robust API design standards (REST/contract-first), ensuring versioning, backward compatibility, and partnering with consumers for usability and reliability.

• Lead UI engineering efforts for critical user experiences, focusing on building scalable front-end components, optimizing performance, ensuring accessibility, and maintaining design consistency.

• Write secure, high-quality, production-ready code (Python preferred), establishing engineering excellence through rigorous design and code reviews, and creating reference implementations for both backend and UI.

📝 Enhancement Note: This role is positioned as a "Sr Lead Software Engineer" within "Cloud Foundation Services," indicating a senior individual contributor role with significant architectural and technical leadership responsibilities. The emphasis on ADK/LangGraph and Agentic AI suggests a focus on cutting-edge AI development within a large, established financial institution. The "Cloud Foundation Services" context implies a focus on building and maintaining the underlying infrastructure and platforms that support other engineering teams.

📈 Primary Responsibilities

• Lead the architecture and end-to-end implementation of complex software solutions, including system design, component design, integration patterns, and technical decision-making across multiple teams.

• Own hands-on delivery of production AI/agentic applications using ADK and LangGraph, including orchestration workflows, tool/function calling, and multi-step stateful graphs.

• Define and enforce API design standards (REST/contract-first), including versioning and backward compatibility, and partner with consumers to ensure usability and reliability.

• Lead UI engineering for key user experiences, including building scalable front-end components, ensuring performance, accessibility, and consistency with design standards.

• Write secure, high-quality, production-ready code (Python preferred) and set the bar through design reviews, code reviews, and reference implementations across backend and UI.

• Drive system resiliency and operational readiness, including performance tuning, observability, incident response participation, and durable remediation of recurring issues.

• Build and maintain AI application quality controls and guardrails appropriate for production use.

• Partner with cross-functional teams (Product, Design, Security, SRE, and peer engineering teams) to translate requirements into well-designed APIs and intuitive, reliable user experiences.

• Drive improvements across the SDLC toolchain (CI/CD, test automation, security scanning, dependency management) to enhance delivery velocity, quality, and governance.

📝 Enhancement Note: The responsibilities highlight a blend of deep technical execution and leadership. The explicit mention of "production AI/agentic applications using ADK and LangGraph (mandatory)" underscores the critical nature of this specific AI framework. The emphasis on "system resiliency," "operational readiness," "performance tuning," and "observability" points to a strong focus on robust, enterprise-grade software delivery, typical for a financial institution. Driving improvements across the SDLC toolchain indicates a proactive approach to optimizing engineering processes.

🎓 Skills & Qualifications

Education: Formal training or certification on software engineering concepts and 5+ years applied experience.

Experience: 5+ years of applied software engineering experience with a strong track record in enterprise environments.

Required Skills:

• Expert-level proficiency in Python and extensive experience building backend services in enterprise environments.

• Strong hands-on UI engineering experience building modern web applications (e.g., React) with a focus on performance, maintainability, and user experience.

• Hands-on production experience building AI applications using ADK and LangGraph, including operating and maintaining them in production environments.

• Proven experience in system design, application development, testing, and ensuring operational stability for production services.

• Strong experience with API design and implementation using FastAPI (or equivalent), including REST service and microservice development best practices.

• Deep understanding of software design principles and patterns, with the ability to apply them consistently across large-scale systems.

• Experience with relational databases (e.g., Postgres) including strong SQL fundamentals and the ability to design for performance and reliability.

• Experience developing, debugging, and maintaining code in a large corporate environment using modern programming languages and database querying languages.

• Solid understanding of agile methodologies and engineering practices such as CI/CD, application resiliency, performance engineering, and security-by-design.

• Ability to lead complex technical discussions and drive alignment across stakeholders and teams, covering design reviews, trade-offs, delivery plans, and risk management. Preferred Skills:

• Proficiency in coding in one or more languages beyond Python and ability to learn new technologies quickly; capability to guide others in adoption, best practices, and standards.

• Hands-on experience building and operating cloud-based applications on AWS environments.

• Hands-on experience with containers and CI/CD practices in production environments.

📝 Enhancement Note: The requirement for "Formal training or certification on software engineering concepts and 5+ years applied experience" is a standard baseline for senior roles. The "mandatory" nature of ADK/LangGraph experience is a key differentiator. The preference for AWS experience aligns with modern cloud-native development practices, common in large enterprises seeking scalability and efficiency. The emphasis on "security-by-design" and "operational stability" is critical for the financial services industry.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate end-to-end software development lifecycle (SDLC) ownership for complex projects, showcasing contributions from design to production deployment and maintenance.

• Showcase specific examples of AI application development using ADK and LangGraph, detailing the architecture, implementation challenges, and production outcomes.

• Present case studies of API design and implementation, highlighting adherence to RESTful principles, versioning strategies, and successful integrations with consuming services.

• Include examples of modern UI development, focusing on component-based architecture (e.g., React), performance optimizations, and user experience enhancements.

• Provide evidence of contributions to system design and architecture, illustrating problem-solving approaches for scalability, reliability, and security in enterprise environments. Process Documentation:

• Detail the process for designing and implementing secure, production-ready APIs, including contract-first approaches and versioning strategies.

• Document workflows for building and maintaining AI applications, emphasizing orchestration, tool integration, and quality guardrails for production environments.

• Illustrate processes for ensuring system resiliency and operational readiness, including observability, performance tuning, and incident remediation.

• Outline approaches for improving SDLC tooling and practices, such as CI/CD pipeline enhancements, test automation strategies, and security scanning integration.

📝 Enhancement Note: For a "Sr Lead Software Engineer" role, a portfolio should go beyond just code samples. It needs to demonstrate architectural thinking, leadership in process improvement, and a deep understanding of production-grade software delivery. The emphasis on ADK/LangGraph and AI applications means specific examples in this domain are crucial. Demonstrating how one drives improvements in SDLC processes is also a key differentiator for senior engineering roles.

💵 Compensation & Benefits

Salary Range: Based on industry benchmarks for Sr Lead Software Engineers in Plano, TX, with 5-10 years of experience, particularly within large financial institutions, the estimated salary range is between $150,000 - $200,000 annually. This range accounts for the specialized skills in Python, AWS, UI development, and particularly the mandatory ADK/LangGraph and Agentic AI expertise.

Benefits:

• Comprehensive health care coverage (medical, dental, vision).

• Retirement savings plan (e.g., 401(k) with company match).

• Backup childcare services.

• Tuition reimbursement for continued education and professional development.

• Mental health support programs and resources.

• Financial coaching services.

• On-site health and wellness centers.

Working Hours: 40 hours per week, standard business hours. While the role is on-site, there may be flexibility expected for critical incident response or project deadlines, common in enterprise software engineering.

📝 Enhancement Note: The salary estimate is derived from market data for senior software engineering roles in the Dallas-Fort Worth metroplex, factoring in the specialized AI/ML skills and the prestige of JPMorgan Chase & Co. The benefits listed are directly from the provided company data and are comprehensive, reflecting typical offerings for large corporations in the financial sector. The working hours are standard, but the nature of enterprise software development often implies on-call responsibilities or extended hours during critical periods.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services. JPMorgan Chase & Co. is a leading global financial services firm with a history spanning over 200 years, offering a wide range of financial solutions. This industry context implies a strong emphasis on security, compliance, stability, and data integrity.

Company Size: Large Enterprise (likely 100,000+ employees globally). This size suggests a structured environment with established processes, significant resources, and opportunities for specialization and career advancement across various departments.

Founded: Over 200 years ago. This long history signifies stability, deep-rooted expertise, and a commitment to long-term innovation and client relationships.

Team Structure:

• The role is within "Cloud Foundation Services," suggesting a central platform engineering team responsible for providing core cloud infrastructure, tools, and services to other engineering teams across the organization.

• The team likely comprises specialized engineers focusing on different aspects of cloud infrastructure, AI platforms, and developer tooling.

• Reporting structure is likely within a larger technology division, with potential for mentorship and leadership over other senior engineers or contributing to architectural guilds. Methodology:

• Agile methodologies are standard, with an emphasis on iterative development, rapid feedback loops, and continuous improvement.

• A strong focus on secure coding practices, operational readiness, and robust testing is paramount due to the financial industry's stringent requirements.

• Data-driven decision-making is expected, leveraging metrics from system performance, user engagement, and development velocity to guide improvements.

Company Website: https://www.jpmorganchase.com/

📝 Enhancement Note: The company's long history and position in the financial services industry are critical context. This means the "Cloud Foundation Services" team operates under strict regulatory and security compliance. The culture likely balances innovation with stability, prioritizing robust, well-tested solutions over rapid, unverified change. The emphasis on "agile" should be understood within this regulated framework.

📈 Career & Growth Analysis

Operations Career Level: This "Sr Lead Software Engineer" role represents a senior individual contributor path focused on deep technical expertise and architectural leadership. It's a step beyond a standard senior engineer, implying responsibility for setting technical direction, mentoring, and driving significant architectural decisions. This role is crucial for building and maintaining foundational technology services that impact numerous other teams and products.

Reporting Structure: The role reports into leadership within Cloud Foundation Services, likely a Director or VP of Engineering. The engineer will collaborate closely with Product Managers, Architects, SREs, and peer engineering teams to define and deliver on technical roadmaps. Direct reports are not explicitly mentioned, suggesting an IC leadership track, but mentorship of junior and mid-level engineers is expected.

Operations Impact: The impact of this role is substantial, influencing the stability, scalability, and efficiency of the firm's foundational cloud services. By developing robust AI applications and core platform components, this engineer directly contributes to the firm's ability to innovate, serve clients effectively, and maintain a competitive edge in the financial technology landscape. Improvements in AI capabilities and developer tooling can lead to faster product delivery and enhanced client experiences across the organization.

Growth Opportunities:

• Technical Specialization: Deepen expertise in Agentic AI, ADK/LangGraph, cloud-native architectures (AWS), and API design, becoming a recognized subject matter expert within the firm.

• Architectural Leadership: Progress into Principal or Distinguished Engineer roles, focusing on firm-wide architectural strategy, setting technical standards, and leading major technology initiatives.

• Management Track: Transition into engineering management roles, leading teams, managing people, and driving strategic execution, leveraging the technical foundation built in this role.

• Cross-functional exposure: Gain broad experience by working with diverse product teams and business units, understanding their technology needs and contributing to their success through foundational services.

📝 Enhancement Note: The "Sr Lead" title signifies a critical juncture in a technical career. It's a path for engineers who want to remain hands-on and technically influential rather than moving into people management. The growth opportunities reflect this, emphasizing deep technical mastery and architectural influence. The impact is measured by the enablement of other engineering teams and the robustness of the core services provided.

🌐 Work Environment

Office Type: On-site. The role requires regular presence at the Plano, TX office location.

Office Location(s): 8181 Communications Pkwy Bldg F, Plano, TX 75024. This location is within a major business hub in the Dallas-Fort Worth metroplex.

Workspace Context:

• The workspace is likely a modern office environment designed for collaboration, with dedicated desks and shared spaces for team meetings and brainstorming.

• Access to high-performance computing resources, secure network infrastructure, and standard office amenities will be provided to support development and operational tasks.

• Opportunities for regular interaction with a diverse team of engineers, product managers, and architects within the Cloud Foundation Services group and other collaborating departments.

Work Schedule: The standard work schedule is 40 hours per week, typically during core business hours to facilitate team collaboration and meetings. However, given the critical nature of financial services and cloud infrastructure, occasional flexibility may be required to address urgent production issues, system maintenance, or project deadlines.

📝 Enhancement Note: An on-site role at a major financial institution like JPMorgan Chase in Plano suggests a professional, structured work environment. While collaboration is key, the emphasis on security and stability might mean a more controlled approach to remote work policies compared to some tech-centric startups. The Plano location is a significant corporate hub, indicating a well-resourced office environment.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or recruiter call to assess basic qualifications, experience, and cultural fit.

• Technical Phone Screen: A 45-60 minute interview with a senior engineer focusing on core concepts in Python, system design, AI/ML fundamentals, and potentially ADK/LangGraph basics.

• On-site/Virtual Loop (Multiple Rounds):

  • System Design: A deep dive into designing scalable, resilient systems, possibly focusing on cloud architecture or AI application infrastructure.
  • Coding Challenge: Live coding session(s) in Python, testing proficiency in algorithms, data structures, and problem-solving. May involve backend or UI-related tasks.
  • AI/Agentic AI Focus: A session dedicated to discussing experience with ADK/LangGraph, agentic AI concepts, and how to build/deploy production AI systems. May include scenario-based questions.
  • Behavioral/Leadership Interview: Assessing leadership qualities, ability to drive technical decisions, mentor others, and collaborate effectively with cross-functional teams.
  • Manager/Hiring Lead Interview: Discussing career aspirations, team fit, and overall alignment with the role and company.

• Portfolio Review: Candidates may be asked to present specific projects from their portfolio, detailing their technical contributions, architectural decisions, and the impact of their work.

Portfolio Review Tips:

• Highlight ADK/LangGraph Projects: Showcase at least one significant project demonstrating hands-on experience with ADK and LangGraph in a production or near-production context. Detail the problem solved, the architecture, and the outcomes.

• Demonstrate System Design Acumen: Include case studies of complex systems you've designed or significantly contributed to. Focus on scalability, fault tolerance, and security.

• Showcase Python Backend & UI Skills: Present examples of robust backend services (FastAPI preferred) and modern UI applications (React preferred) you've built. Emphasize code quality, performance, and user experience.

• Quantify Impact: Whenever possible, use metrics to demonstrate the value of your work (e.g., improved performance by X%, reduced latency by Y%, increased user engagement by Z%).

• Explain Technical Trade-offs: Be prepared to discuss the rationale behind your technical decisions, including the trade-offs considered (e.g., performance vs. cost, complexity vs. maintainability).

Challenge Preparation:

• System Design: Practice designing distributed systems, microservices architectures, and cloud-native solutions. Familiarize yourself with common patterns for scalability, availability, and resilience.

• Python Proficiency: Brush up on Python fundamentals, common libraries, and best practices for writing clean, efficient, and maintainable code. Practice coding problems on platforms like LeetCode.

• AI/Agentic AI Concepts: Review core concepts of Large Language Models (LLMs), prompt engineering, agentic workflows, tool usage, and state management in AI applications. Understand the specifics of ADK and LangGraph.

• API Design: Revisit RESTful API design principles, OpenAPI specifications, and strategies for versioning and backward compatibility.

• Behavioral Questions: Prepare STAR method (Situation, Task, Action, Result) responses for common leadership, teamwork, and problem-solving questions.

📝 Enhancement Note: The interview process described is typical for senior engineering roles at large financial institutions, emphasizing rigorous technical evaluation, system design capabilities, and behavioral assessment. The explicit focus on ADK/LangGraph and Agentic AI means candidates must prepare specific examples and discussions around these technologies. A strong portfolio demonstrating these skills is crucial for success.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (expert-level, preferred), potentially others for broader skill demonstration.

• AI/ML Frameworks: ADK (Agent Development Kit), LangGraph (mandatory for production AI/agentic applications).

• Web Frameworks: FastAPI (for API design and implementation), React (for modern UI engineering).

• Databases: PostgreSQL (strong SQL fundamentals, performance and reliability design).

• Cloud Platform: AWS (preferred for cloud-based application development and operation).

• Containerization: Docker, Kubernetes (preferred for CI/CD and production environments).

Analytics & Reporting:

• Observability Tools: For monitoring application performance, logging, and tracing in production environments.

• CI/CD Tools: Jenkins, GitLab CI, or similar for automated build, test, and deployment pipelines.

• Testing Frameworks: Pytest for Python, Jest/React Testing Library for UI.

CRM & Automation: While not explicitly mentioned for this role, understanding how foundational services integrate with enterprise systems, potentially including CRM or other business applications, is beneficial.

📝 Enhancement Note: The technology stack is highly specific, with Python, AWS, ADK, LangGraph, FastAPI, and React being central. Proficiency in these tools is non-negotiable. The emphasis on "production readiness," "observability," and "CI/CD" indicates a need for experience in robust, automated software delivery pipelines common in enterprise settings.

👥 Team Culture & Values

Operations Values:

• Excellence & Quality: A commitment to delivering high-quality, secure, and reliable software, setting high standards for code, design, and operational practices.

• Innovation & Continuous Improvement: Driving advancements in AI capabilities and developer experience, while consistently seeking ways to optimize processes and technologies.

• Collaboration & Teamwork: Working effectively with cross-functional teams, sharing knowledge, and contributing to a supportive and inclusive team environment.

• Accountability & Ownership: Taking full responsibility for the design, delivery, and operational health of the systems developed.

• Security & Compliance: Upholding the highest standards of data security and regulatory compliance, integral to financial services operations.

Collaboration Style:

• Cross-functional Integration: Actively partnering with Product, Design, Security, and SRE teams to ensure solutions meet diverse requirements and operational standards.

• Mentorship and Knowledge Sharing: Proactively mentoring junior engineers and sharing technical expertise through code reviews, design discussions, and documentation.

• Data-Driven Feedback: Utilizing metrics and performance data to drive discussions and improvements, fostering a culture of continuous learning and adaptation.

📝 Enhancement Note: The values align with a high-performing, enterprise-level engineering team within a regulated industry. Emphasis on "security & compliance" and "accountability & ownership" are paramount for JPMorgan Chase. The collaborative style emphasizes working with diverse stakeholders to achieve common goals, which is typical for complex platform services.

⚡ Challenges & Growth Opportunities

Challenges:

• Integrating bleeding-edge AI (ADK/LangGraph) into a large, regulated enterprise environment: Balancing innovation with strict security, compliance, and legacy system integration requirements.

• Ensuring production-grade reliability and scalability for AI applications: Moving beyond experimental phases to deliver robust, performant, and fault-tolerant AI services.

• Driving adoption and standardization of new technologies across diverse engineering teams: Influencing and guiding other teams to leverage foundational services and best practices effectively.

• Keeping pace with rapid advancements in AI and cloud technologies: Continuously learning and adapting to new tools, frameworks, and methodologies to maintain a competitive edge.

Learning & Development Opportunities:

• Specialized AI/ML Training: Access to internal and external training programs focused on advanced Agentic AI, LLMs, and specific frameworks like ADK/LangGraph.

• Cloud Certifications: Opportunities to pursue AWS certifications to deepen cloud expertise.

• Industry Conferences & Workshops: Participation in relevant technology conferences and workshops to stay abreast of industry trends and network with peers.

• Mentorship Programs: Access to senior technical leaders for guidance and career development, as well as opportunities to mentor others.

📝 Enhancement Note: The challenges are directly tied to the role's focus on cutting-edge AI within a large, traditional financial institution. The growth opportunities are geared towards deep technical mastery and staying current in rapidly evolving fields.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex software system you architected or significantly contributed to. What were the key technical challenges, design decisions, and trade-offs you made, especially concerning scalability and reliability?" (Focus on demonstrating system design principles and production readiness).

• "Walk me through a project where you implemented an AI application using ADK/LangGraph. What was the problem, your approach to orchestration and tool integration, and how did you ensure its production readiness and quality?" (Highlight mandatory skills and practical application).

• "How do you approach API design and management in a large enterprise? Discuss your experience with RESTful principles, contract-first design, and ensuring backward compatibility for diverse consumers." (Assess API expertise and enterprise context).

• "How do you balance driving innovation with ensuring security and compliance, particularly in a regulated industry like financial services?" (Gauge understanding of industry constraints and risk management). Company & Culture Questions:

• "What interests you about JPMorgan Chase & Co. and specifically our Cloud Foundation Services team?" (Prepare to articulate alignment with company mission and team focus).

• "How do you approach mentoring junior engineers or leading technical discussions within a team?" (Demonstrate leadership and collaboration skills).

• "Describe a time you encountered significant resistance to a technical proposal or new technology. How did you handle it, and what was the outcome?" (Assess influence and stakeholder management). Portfolio Presentation Strategy:

• Structure with Impact: Begin with a high-level overview of your career and key areas of expertise, then dive into 2-3 of your most relevant projects.

• Focus on ADK/LangGraph: Dedicate significant time to showcasing your experience with agentic AI. Clearly explain the problem, your solution architecture, implementation details, and quantifiable results. Use diagrams where helpful.

• Demonstrate System Design & API Skills: For other projects, highlight your contributions to system architecture, API design (FastAPI), and backend/UI development (Python/React).

• Quantify Results: Use metrics to illustrate the impact of your work (e.g., performance improvements, efficiency gains, successful production deployments).

• Explain Trade-offs: Be ready to discuss the 'why' behind your technical choices, including any challenges and lessons learned.

📝 Enhancement Note: Interview preparation should heavily emphasize the mandatory ADK/LangGraph and Agentic AI requirements, alongside core software engineering and system design skills. Candidates must be ready to discuss their experience in the context of a large, regulated financial institution.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided Oracle Cloud job portal link.

• Tailor your resume: Highlight your expert-level Python skills, hands-on experience with ADK/LangGraph, and any relevant AWS or enterprise system design accomplishments. Ensure keywords like "Agentic AI," "LangGraph," "FastAPI," "React," and "System Design" are prominently featured.

• Prepare your portfolio: Curate examples that specifically showcase your ADK/LangGraph projects, complex system designs, and API development work. Be ready to discuss these in detail during interviews.

• Research JPMorgan Chase & Co.: Understand the company's mission, values, and its position in the financial services industry. Familiarize yourself with "Cloud Foundation Services" and its role within the organization.

• Practice technical and behavioral questions: Rehearse responses to common interview questions, particularly those focusing on AI application development, system architecture, and leadership in an enterprise setting.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.