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.
Process and analyze large datasets that support AI solutions.
Develop components for concurrent, multi-user, and real-time applications.
Write secure, high-quality production code and deploy on AWS using CI/CD.
Partner with business users to gather feedback and iterate quickly, while contributing to engineering communities of practice and fostering a culture of diversity, opportunity, inclusion, and respect.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years of applied experience.
Strong Python skills and hands-on experience with Gen AI and AI/ML frameworks, paired with a bias for action and comfort working to tight timelines.
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.
Hands-on experience with agent frameworks such as LangGraph, Google ADK, or similar.
Practical experience building LLM-based applications using prompt engineering, skill and tool development, and RAG.
Experience evaluating and benchmarking models or LLM-based applications.
Experience working with large datasets.
Understanding of concurrent, multi-user, and real-time application design.
Experience with AWS or another major cloud platform.
Strong understanding of the SDLC and agile practices, including CI/CD, application resiliency, and security.
Preferred qualifications, capabilities, and skills
Exposure to SLMs, LRMs and model fine-tuning.
Hands-on AI projects, hackathons or personal builds.
Familiarity with vector databases and streaming architectures.
Front-end experience for AI user experiences.
