Lead Software Engineer Fullstack JavaAWSAIML

We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.

As a Lead Software Engineer at JPMorganChase within the Commercial & Community Banking youare 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 firms 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 ) 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)