Lead Software Engineer - Java, Spring boot, Microservices , Real time application

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Lead Software Engineer - Java/Springboot/Kubernetes at JPMorgan Chase within the Corporate Sector, you play a crucial role in an agile team dedicated to enhancing, building, and delivering reliable, market-leading technology products in a secure, stable, and scalable manner. Your expertise and contributions promote significant business impact, utilizing deep technical knowledge and problem-solving skills to address a wide range of challenges across multiple technologies and applications. This position is ideal for someone passionate about solving business problems through innovative engineering practices. The team leverages a variety of cutting-edge tools and technologies to optimize critical business processes, including low latency and high throughput API development, cloud technologies, and big data solutions.

Excellent opportunity for someone who is passionate around solving business problems through innovation and engineering practices. The team is using variety of latest tools and technologies to address critical business processes. This includes low latency & high throughput API Development, Cloud Technologies, Big Data solutions.

Job responsibilities

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• Develops secure and high-quality production code, and reviews and debugs code written by others
• Drives decisions that influence the product design, application functionality, and technical operations and processes
• Serves as a function-wide subject matter expert in one or more areas of focus.
• 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.
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
• Influences peers and project decision-makers to consider the use and application of leading-edge technologies
• Adds to the team culture of diversity, opportunity, inclusion, and respect

• Formal training or certification on software engineering concepts and 8+ years applied experience
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Advanced in one or more programming language(s) including Java, Springboot, Microservices, RESTful API and Kubernetes
• Experience in API Gateways, Apache Kafka, Cassandra or other NoSQL DB, exposure to cloud infrastructure
• Maven build and dependency management. Docker and Kubernetes fundamentals for deploying/supporting services
• Expertise with observability tools such as Splunk, Kibana, AppDynamics or Dynatrace.
• 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 practicesl processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• Ability to tackle design and functionality problems independently with little to no oversight.Practical cloud native experience..
• Hands-on exposure to broader Big Data ecosystems, such as Databricks, EMR, and large-scale batch or stream-processing platforms

• Experience with AWS public cloud
• Hands-on experience with Apache Spark for distributed data processing and large-scale workloads
• Proficiency in Python for automation, data processing, and service/module development
• Experience working with large datasets in distributed environments, including partitioning, performance tuning, and job reliability