• Design, develop and deploy SaaS services end-to-end—from understanding requirements and designing solutions to deployment, production support, scalability and performance • Be on the fore front of AI-native SDLC by using agents throughout design, coding, testing, and documentation • Research new technologies and solutions to improve existing products and explore new directions • Implement comprehensive automated testing at the component level and collaborate with SDET(s) • Participate in standards bodies and shape next-generation standards like W3C, WebAuthn, and OAuth • Work directly with customers - product calls to understand the problems and an on-call rotation of incident escalation

• Apply Generative AI techniques to solve complex business problems by identifying opportunities where AI can enhance existing software systems and create new intelligent features. • Integrate Generative AI models and capabilities into production software applications, bridging the gap between Generative AI research and practical software engineering implementation. • Evaluate and select appropriate Generative AI models, tools, and techniques for specific use cases, considering factors like performance, cost, maintainability, and business impact. • Write high-quality code that combines traditional software engineering with AI components, ensuring robust integration, proper error handling, and comprehensive test coverage. • Review and translate product requirements into technical solutions that leverage AI effectively, designing systems that seamlessly blend AI capabilities with core application functionality. • Lead design and code reviews for AI-enhanced applications, ensuring best practices in both software engineering and AI implementation while maintaining Mercury standards. • Lead multiple sprint teams to deliver AI-powered features and improvements, addressing technical challenges in integrating AI into existing software systems. • Collaborate with product managers, data scientists, and engineering teams to identify AI opportunities and translate them into practical software solutions. • Mentor engineers on how to effectively incorporate AI into their software development practices, promoting understanding of AI capabilities and limitations. • Drive practical AI adoption across teams by demonstrating how to effectively combine software engineering principles with AI technologies. • Collaborate with product managers to estimate effort for AI-enhanced features, prioritize AI integration opportunities, and deliver measurable business value through AI implementation.

• Lead the development of innovative AI-native cloud systems from concept to beta product • Design and implement cloud-based products and prototypes, with a focus on leveraging GenAI technologies • Drive deep technical collaboration with Engineering, Product Management, and Research teams to translate ambiguous business challenges into high-impact, scalable solutions • Drive the model development process through all stages, from initial research to production-ready implementations • Work closely with engineering teams to ensure smooth integration of solutions into production environments • Stay current with the latest advancements in AI-related cloud technologies • Act as a domain expert and mentor for other engineers, raising the bar for cloud engineering and AI innovation across the organization

• Perform comprehensive QA/QC reviews of utility transmission design packages to ensure compliance with client specifications, industry codes (NESC, NEC), and engineering standards. • Validate design accuracy, completeness, and constructability, including reviewing design checklists, redlines, field data, and structural calculations. • Identify design inconsistencies, omissions, and potential constructability issues; provide clear corrective feedback to design teams. • Collaborate with project managers, design engineers, drafters, and field personnel to resolve design discrepancies and ensure timely corrections. • Develop and maintain QA/QC procedures, workflows, and best practices to improve design quality and reduce rework. • Track QA/QC metrics and maintain documentation of review findings, trends, and corrective actions. • Assist in training and mentoring junior engineers and designers on quality standards and client requirements. • Support pre-construction coordination by addressing QA/QC concerns that may impact field execution. • Provide technical recommendations to management for continuous improvement of QA/QC processes and standards. • Ensure adherence to project schedules by maintaining efficient QA/QC turnaround times without compromising quality. • Participate in client meetings, audits, and internal reviews related to quality control and engineering standards compliance. • Assist in the implementation of digital QA/QC tools and systems to streamline review workflows and reporting.