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 what's possible.

As a Lead Software Engineer at JPMorganChase within the Consumer and Community Banking - Deposits 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

• 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