Lead Software Engineer - Fullstack Java/AWS/AI/ML

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Lead Software Engineer - Fullstack Java/AWS/AI/ML

lead · Technology / Software Development

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 JPMorganChase within the Commercial & Community Banking, 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 break down technical problems
• 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.
• Full Stack Application Development Develop and maintain front-end applications using modern JavaScript frameworks (e.g., React, Angular, or Vue.js), build robust backend services in Java and Python, and ensure seamless integration between UI layers, APIs, middleware, and data stores.
• AI/ML Model Development & Edge Deployment Build, train, fine-tune, and optimize AI/ML models using industry-standard tools and frameworks (e.g., PyTorch, TensorFlow, Hugging Face, ONNX, TensorRT). Package and deploy models for inference on edge devices with constrained compute resources, as well as in cloud-hosted backend environments. Manage the full model lifecycle including experimentation, versioning, evaluation, and monitoring.
• Cloud Infrastructure & Operations Leverage hands-on AWS expertise to provision, configure, and manage cloud infrastructure supporting data pipelines and model serving. This includes working with services such as EC2, S3, Lambda, ECS/EKS, SageMaker, Kinesis, IAM, CloudWatch, and related tooling. Ensure operational excellence through monitoring, alerting, cost optimization, and infrastructure-as-code practices.
• Data Pipeline Development & Integration Design, implement, and maintain scalable, fault-tolerant data pipelines that ingest, transform, and deliver data across distributed systems. Architect event-driven and streaming data flows using Apache Kafka and other messaging queue technologies to support real-time and near-real-time processing requirements.
• Collaboration & Technical Leadership Partner with data scientists, product managers, and platform teams to translate business requirements into technical solutions. Participate in architecture reviews, code reviews, and contribute to engineering best practices and standards.

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
• Programming Languages & Frameworks: Proficiency in JavaScript/TypeScript (including modern front-end frameworks), Java (Spring Boot or similar), and Python. Demonstrated ability to work fluidly across all three ecosystems.
• Data Streaming & Messaging: Strong experience with Kafka (producers, consumers, Kafka Streams, Connect, Schema Registry) and familiarity with other messaging systems (e.g., RabbitMQ, AWS SQS/SNS).
• AI/ML Model Development: Hands-on experience building and training machine learning models using frameworks such as PyTorch, TensorFlow, or JAX. Familiarity with model optimization and conversion tools for edge deployment (e.g., ONNX Runtime, TensorRT, TFLite, Core ML). Experience with experiment tracking tools such as MLflow or Weights & Biases.
• AWS Cloud Operations: Demonstrated hands-on experience operating production workloads on AWS, including provisioning infrastructure, managing deployments, troubleshooting issues, and implementing CI/CD pipelines in a cloud-native environment.
• Data Engineering: Experience building ETL/ELT pipelines and working with both structured and unstructured data at scale. Familiarity with tools such as Apache Spark, Airflow, or Step Functions is a plus.
• Software Engineering Practices: Strong foundation in version control (Git), containerization, orchestration, automated testing, and CI/CD tooling.

• Strong observability and reliability background (metrics/logs/traces, SLOs/SLIs, incident response, performance tuning, and root-cause analysis)
• Experience with Infrastructure-as-Code and policy-as-code (e.g., Terraform/CloudFormation, automated guardrails, secrets management)
• Familiarity with data governance and security practices in regulated environments (PII handling, encryption, IAM least privilege, secure SDLC)
• Experience building event-driven microservices and distributed systems patterns (idempotency, exactly-once/at-least-once tradeoffs, schema evolution, backpressure)
• Experience designing reliable event-driven and messaging queue architectures using SQS / KAFKA (DLQs, replay/backfill strategies, ordering guarantees, idempotency, and data retention/compaction)