Lead Software Engineer - Java, AWS, AI

Job responsibilities

• Design and develop robust, scalable Java-based applications that meet complex enterprise requirements across multiple business domains
• Lead the architecture and implementation of cloud-native solutions on AWS, ensuring high availability, security, and performance
• Drive the adoption and continuous improvement of CI/CD pipelines, enabling faster, more reliable software delivery
• 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
• Collaborate with cross-functional teams including product, architecture, and business stakeholders to translate requirements into technical solutions
• Conduct thorough code reviews and establish engineering best practices that elevate the quality and consistency of the team's output
• Mentor and coach junior engineers, fostering a culture of learning, ownership, and technical growth

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and advanced applied experience
• Advanced proficiency in Java, including experience building and maintaining large-scale, production-grade applications
• Hands-on experience designing and deploying cloud-native solutions on AWS (e.g., EC2, S3, Lambda, RDS, EKS)
• Demonstrated experience building and maintaining CI/CD pipelines using industry-standard tools
• Strong understanding of software design patterns, distributed systems, and microservices architecture
• Proven ability to lead technical discussions, influence design decisions, and drive alignment across engineering teams
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices

Preferred qualifications, capabilities, and skills

• Experience with infrastructure-as-code tools such as Terraform or AWS CloudFormation
• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes
• Exposure to observability and monitoring practices using tools such as Datadog, Splunk, or equivalent platforms
• Experience contributing to or leading architectural decisions in a large, matrixed enterprise environment
• Knowledge of security best practices in cloud and application development contexts

About Company

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