Required Qualifications
• Strong software engineering background with hands on experience (Python, Java, C#, or similar).
• Practical experience building Generative AI solutions in production environments.
• Experience with GitHub Copilot and AI assisted development workflows.
• Familiarity with LLMs, prompt engineering, embeddings, and RAG patterns.
• Proven ability to evaluate and operationalize emerging technologies.
Preferred Qualifications
• Experience with autonomous or agentic AI programming.
• Hands on experience with Microsoft Copilot Studio and Power Platform.
• Background in Developer Experience (DX), platform engineering, or productivity tooling.
• Experience working in large scale or regulated enterprise environments.
What Success Looks Like
• Measurable improvements in developer velocity and workflow efficiency.
• Broad adoption of Copilot based development practices.
• Reduced friction across the software development lifecycle.
• Scalable, reusable AI patterns adopted across teams.
Why This Role Matters
• You will shape how AI transforms software development at scale.
• You’ll influence modern, Copilot first engineering workflows.
• Your work will directly enable engineers to focus on higher value problem solving.
• You’ll help define the future of AI driven developer productivity.
• This role researches, evaluates, and supports the adoption of emerging technologies, including identifying effective AI use cases to drive
efficiency.
• It applies core software engineering principles to design, develop, test, integrate, and maintain applications that deliver business
value.
• The role manages the full system development lifecycle, ensuring solutions are delivered on time, within budget, and successfully
implemented.
• It provides deep functional and subject matter expertise, interpreting business challenges and recommending best practices to
solve complex problems.
• While primarily an individual contributor, the role may act as a player coach, leading projects or teams and operating
independently with guidance only in the most complex situations.
Agile Engineering Best Practices
• Identifies and recommends improvements to the software development lifecycle and best practices in agile engineering.
• Provides input into various software development strategies (e.g., unit testing, acceptance testing, continuous delivery and integration,
release and deployment for frequent and reliable release), providing the case for using agile vs. traditional methodologies.
• Develops the roadmap and guidelines for tailoring agile practices to a complex and dynamic software development project.
Project Management
• Prioritizes multiple project plans, making final decisions about priorities, timing and resources.
• Keeps abreast of the progress of multiple projects within a program or across the department; coaches project managers to adjust priorities or timelines as needed.
• Monitors resource usage across projects or programs, identifying areas or synergy or redundancy and looking for ways to optimize efficiency.
Troubleshooting
• Provides guidance and expertise to analyze data and patterns and assess the root cause of complex, non-routine problems.
• Collaborates with others across disciplines to brainstorm and execute solutions to the most complex, uncommon problems.
• Guides the execution of solutions for complex, non-routine issues; redirects solution implementation if desired results were not originally met.