Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
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 3+ years applied experience.
Strong application development skills with exposure to operational stability in production environments and hands-on experience in Java Full Stack Development.
Experience with system design fundamentals, microservices patterns, and API development (RESTful and/or streaming).
Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure)
Proficiency with data technologies (relational and/or NoSQL) and common observability practices.
Practical familiarity with LLMs / generative AI concepts and use cases (e.g., RAG, tool/prompt orchestration, guardrails/evaluation); working knowledge of Python for AI/ML integrations.
Overall knowledge of the Software Development Life Cycle, and solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
Familiarity with Docker, Kubernetes, Helm, modern CI/CD practices, multi-region service deployments, and zero-downtime release strategies.
Strong communication skills, ownership mindset, proactive approach to continuous improvement, and a track record delivering scalable, reliable, and secure products from concept to launch.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
Preferred qualifications, capabilities, and skills
Cloud certification in AWS, GCP, or Azure.
Working knowledge of Python (for AI/ML integrations) is a plus.
Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
Experience with multi-region service deployments and zero-downtime release strategies.
Strong communication skills, ownership mindset, and a proactive approach to continuous improvement.
Track record delivering scalable, reliable, and secure products from concept to launch.
Working knowledge of Python (for AI/ML integrations) is a plus.
