Lead Software Engineer – Cloud DevOps & AI

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 Consumer and Community Banking - Deposits team 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

• Design and implement CI/CD pipelines infrastructure-as-code (IaC) frameworks and container orchestration strategies leveraging tools such as Kubernetes Docker Terraform and Spinnaker while utilizing AI-driven automation to streamline deployment and management across cloud and on-premises environments.
• Lead the architecture deployment and management of cloud infrastructure in AWS establishing and enforcing best practices for reliability scalability security and cost optimization across all cloud environments.
• Drive the adoption of AI and machine learning capabilities within DevOps workflows including intelligent monitoring predictive analytics and automated remediation while evaluating and integrating AI-powered tools to continuously improve development velocity system reliability and operational efficiency.
• Lead the integration of intelligent agents for workflow automation decision-making and process optimization.
• Develop AI-powered observability solutions to monitor analyze and proactively manage application and infrastructure health automating alerting root cause analysis and incident response using advanced ML techniques.
• Work closely with cross-functional teams including engineering product and operations to identify automation opportunities and deliver impactful solutions.
• Stay abreast of emerging AI/ML technologies frameworks and industry trends driving continuous improvement by evaluating and implementing new tools methodologies and approaches.
• Provide hands-on technical guidance to a team of software and DevOps engineers fostering a culture of innovation accountability and continuous learning.
• Conduct code reviews architectural assessments and design discussions to uphold engineering excellence.
• 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.

Required qualifications capabilities and skills

• Formal training or certification on software engineering concepts and 5 years applied experience
• Experience in AI/ML engineering with proven expertise in agent-based systems and automation.
• Strong experience in automating IAC development (e.g. Terraform Ansible CloudFormation) using AI/ML.
• Deep understanding of observability tools (e.g. Prometheus Grafana ELK stack) and automation using AI/ML.
• Proficiency in Python Java or similar programming languages; experience with ML frameworks (TensorFlow PyTorch Scikit-learn).
• Familiarity with cloud platforms (AWS Azure GCP) and containerization (Docker Kubernetes).
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g. cloud artificial intelligence machine learning mobile etc.)
• 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

• In-depth knowledge of the financial services industry and their IT systems
• Practical cloud native experience