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.
QualificationsBasic 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?
Preferred Qualifications
• Master's degree in Computer Science, AI, or related field.
• Experience building and shipping multi-agent orchestration systems or agentic AI services in production.
• Experience with AI evaluation frameworks (rubrics, golden datasets, judge agents, offline/online eval).
• Experience with Azure AI Foundry, Azure OpenAI, or similar enterprise AI platforms.
• Familiarity with agent security patterns (prompt injection defense, tool access controls, data boundary enforcement).
• Experience leading co-development or cross-org engineering delivery with multiple partner teams.
• Strong business acumen with ability to tie engineering investments to measurable customer and business outcomes (volume reduction, resolution time, CSAT).
• Experience influencing product engineering roadmaps and driving cross-functional collaboration at scale.
• Background in customer support engineering, supportability, or customer experience platforms is a strong plus.
• 8+ years in software engineering or applied AI roles, with 3+ years in people management leading technical engineering teams.
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This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
