Responsibilities• Build and lead the AI engineering pod - Hire, coach, and develop a team of Applied AI Engineers; foster an inclusive, high-trust culture where engineers ship production AI services
with ownership and velocity.
• Own engineering execution for agentic AI services - Drive the end-to-end lifecycle of production AI agents from spec to deployment, including LLM orchestration, multi-agent
workflows, RAG pipelines, and evaluation systems.
• Set technical direction and engineering standards - Define architecture patterns, code quality bar, evaluation frameworks, deployment practices, and observability standards for the AI
engineering pod. Ensure production-quality C# and Python with TDD, CI/CD, staged rollouts, and full observability.
• Own production health and reliability - Ensure deployed AI agents meet quality, performance, and safety standards. Drive incident response, root cause analysis, and continuous
improvement for agent systems in production.
• Build and maintain evaluation systems - Establish evaluation frameworks including rubrics, golden datasets, and judge agents to validate agent correctness and safety before and
after production deployment. Ensure agents graduate through shadow mode to autonomous operation with eval gates at each stage.
• Influence product and platform roadmaps - Partner with product, platform engineering, and Azure service teams to shape the agentic AI platform direction. Translate customer support
patterns and demand signals into engineering priorities.
• Drive measurable business impact - Own KPIs including case volume reduction, automation accuracy, resolution time improvement, and customer satisfaction impact. Use data-driven
insights to set OKRs and demonstrate engineering ROI.
• Ensure responsible AI and compliance - Partner with AI Governance to embed responsible AI practices, PII protection, action boundaries, and audit trails into all agent systems
architecturally.
• Develop engineering talent - Mentor engineers on applied AI engineering craft, including agentic system design, evaluation-driven development, and the judgment to know where
agents should and should not act autonomously. Build career growth paths across AI engineering competencies.
QualificationsRequired qualifications- Bachelor's Degree AND 6+ years experience in low-code application development, engineering product/technical program management, data analysis, or product development OR equivalent experience.
Additional or preferred qualifications- Bachelor's Degree AND 12+ years experience in low-code application development, engineering product/technical program management, data analysis, or product development OR equivalent experience.
- Proficiency in Python and C# with hands-on experience building production AI/ML systems.
- Experience with LLM-based systems, including prompt engineering, RAG architectures, or agent frameworks.
- Experience with cloud platforms (Azure) and cloud-native service development (microservices, containers, CI/CD).
- 3+ years people management and/or informal/indirect team leadership experience.
- 4+ years of experience using low-code/no-code platforms (e.g., Dataverse, Power Applications).
- 3+ years of experience managing and configuring artificial intelligence solutions (e.g., chatbots).
- 4+ years of experience with programming/coding.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
