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Lead Software Engineer - Java Fullstack, Cloud, AI
C
Lead Software Engineer - Java Fullstack, Cloud, AI
Chase BankEarly Applicant
- Posted 3 days ago
- Be among the first 20 applicants
Job Description
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorgan Chase within the Consumer and community banking technology - Business Banking team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
Job responsibilities
As a Lead Software Engineer at JPMorgan Chase within the Consumer and community banking technology - Business Banking team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Develops secure and high-quality production code, and reviews and debugs code written by others
- 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.
- Lead technical design and delivery of complex full-stack capabilities (Java/Spring Boot + React/Angular).
- Drive event-driven patterns using Kafka (idempotency, retries, ordering, error handling).
- Establish strong engineering practices (test strategy, automation, CI/CD quality gates, observability).
- Own production excellence: incident reduction, RCA prevention, performance tuning, and operational readiness.
Mentor engineers and influence technical direction across squads and partner teams.
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
- Deep expertise inJava 8+ / Spring Boot, microservices, andReact/Angular.
- Strong Kafka/event-driven architecture experience (retries, idempotency, DLQ patterns, schema/versioning).
- Experience with SQL databases with data modelling, query optimization, and performance tuning.
- Strong NoSQL experience:Cassandra / DynamoDB / Elastic Search(data modeling, reliability, scale).
- Strong experience withCloud Foundry/AWSand CI/CD strategy and execution.
- Demonstrated leadership in design reviews, mentoring, and cross-team technical alignment.
- Ability to work with tools such as Dynatrace, Splunk, Grafana for observability and monitoring
Experience building applications with resilient, highly available architectures, including consideration for failure modes, recovery, and operational readiness.
Preferred qualifications, capabilities, and skills
- Working knowledge of secure engineering practices and designing to meet Cyber Security and Technology Control requirements (e.g., authentication/authorization, secure APIs, data protection, dependency hygiene).
- Cross-team delivery leadership, PCI experience and AWS cert are pluses.
More Info
Job Type:
Function:
Employment Type:
Key Skills
Test strategy automation
Incident root-cause analysis
CI/CD strategy and execution
Resilient highly available architectures
Observability and monitoring
AI-assisted software development tools
SQL databases
Kafka event-driven architecture
About Company
Skills:
secure coding , Performance Tuning, Spring Boot, Grafana, Angular, Microservices, Automated Testing, React, Dynatrace, Splunk, Data Modelling, Query Optimization, AWS, Test strategy automation, Incident root-cause analysis, CI/CD strategy and execution, Observability and monitoring, AI-assisted software development tools, SQL databases, Kafka event-driven architecture
Skills:
Infrastructure as Code (IaC), Microservices, Gcp, Docker, Rest Apis, Azure, Kubernetes, AWS, orchestration frameworks, LangChain, CrewAI, LLMs, AI-powered workflows, Azure OpenAI, LangGraph, CI CD pipelines, AI agents, OpenAI
Skills:
Large Language Models (LLMs), Machine Learning, Apis, Sql, Microservices, Data Science, MLops, Terraform, Python, AWS, Generative AI, LLMOps, NoSQL databases, Agentic AI systems, Prompt Engineering, Containerized deployments
Skills:
Yaml, Dns, Firewalls, Terraform, Tls, Python, AWS, PowerShell, Bash, Google Cloud, Git, Azure, Kubernetes, Gateways, network segmentation, policy-as-code, Kubernetes RBAC, Go, CI CD, network policies, secrets, private connectivity, HCL, service meshes, egress controls, admission controls
Skills:
Cloud platforms (AWS), Networking, Databases, Git, Memory Management, Linux, System Design, RESTful API development, Python, Multi-threading, Building data pipelines, AI and ML concepts, Vector databases, Hugging Face, Performance aspects of common classes and algorithms, Prompt engineering techniques, LLM APIs, GenAI solutions, Embedding models, CI/CD, LLM frameworks, RAG technologies, I/O concepts