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
Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Excellent system design skills for designing distributed systems with scalability, resiliency, security, and performance best practices.
- Advanced proficiency in Java (17/21+) and Spring Boot for building scalable RESTful microservices.
- Strong command of Kafka for event-driven architectures, including topic design, partitions, consumer groups, and delivery semantics.
Strong experience in Java (Strong hands-on coding expertise), Spring Boot / Spring Framework, AWS Cloud and Kafka (Deep expertise in event streaming and messaging)
In-depth knowledge of Cassandra for distributed data modeling, partition strategy, and query tuning at scale.
Extensive experience with AWS data lake & ETL technologies (S3, Glue, EMR, Lambda, Step Functions) and Maven for build/dependency management.
Solid expertise in containerization and orchestration using Docker and Kubernetes, deploying workloads on AWS EKS and ECS.
Hands-on mastery of Apache Spark (batch + streaming) for large-scale data processing and performance optimization.Proven capability in Jenkins-based CI/CD automation, including pipelines, quality gates, artifact publishing, and deployments.
Effective AI utilization to boost development productivity by leveraging AI agents/skills for coding assistance, test generation, refactoring, documentation, and faster troubleshooting.
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
Preferred qualifications, capabilities, and skills
Familiarity with modern front-end technologies.
Cassandra, Python
