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  Lead system architecture and design for the complex, often ambiguous agentic support platform, using frameworks such as Azure AI Foundry, Microsoft Copilot Studio, or equivalents, while defining the patterns and SDK surfaces other engineers rely on.

• Owning the architecture and run-state reliability of AI-driven support workflows; defining the reliability bar for incident response and live-site health; and serving as a designated responsible individual (DRI) who leads incident retrospectives to identify root causes, owns repair actions, and prevents recurrence across the platform.

• Driving the design patterns that adapt AI workflows to changing support business policies and operational processes (e.g., SLA calculations, case ownership, escalation models).

• Driving customer trust, satisfaction, and sentiment, ensuring AI agents correctly understand intent and guide customers to resolution without degrading experience.

• Providing thought leadership on the security, privacy, and Responsible AI architecture—including rethinking role-based access control (RBAC), data access, case ownership vs. processing, and data exposure—and assuring visible compliance evidence (e.g., audit trails) across products.

• Defining the observability, monitoring, and intervention architecture for multiple AI agents operating concurrently at scale.

• Driving cross-team alignment and establishing scalable engineering standards across engineering, support business, compliance, and platform teams; negotiating and resolving conflicts around dependency ownership; and driving agreements that align partner teams to the delivery schedule for AI-managed support.

• Setting the technical vision for and driving delivery of a platform that enables citizen developers to safely build AI agents for support workflows with reduced barrier to entry.

• Raising the engineering bar across the team by mentoring senior and staff engineers, leading design and code reviews, and establishing best practices for building and evaluating production agentic systems.

• Influencing technical strategy and roadmaps across organizational boundaries, building alignment among partner teams and senior leaders behind a shared architectural vision for AI-managed support.

• Leading the application of automation across production and deployment for complex products, targeting zero-touch deployment, and driving safe change-deployment best practices (e.g., correct flighting, rollback plans) to minimize customer impact.

• Leading experimentation using feature flags/flighting to measure the impact of changes, partnering with Data Science and product managers to define the success and guardrail metrics that drive customer value.

• Partnering with and guiding internal and external stakeholders to anticipate, determine, and confirm customer/user requirements and their feasibility, and advocating for the security and privacy needs of the customers using the platform.

Required/minimun qualifications

Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Other Requirements

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check. This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications

• Experience building production LLM-powered applications, RAG pipelines, prompt engineering, agent frameworks (Semantic Kernel, LangChain), and fine-tuning, with strong judgment on evaluation, latency, and cost trade-offs at scale.

• Experience in AI-native development working within Agent Harnesses (GitHub Copilot CLI, Coding Agents), authoring Markdown specs/ADRs and YAML configs as Agent-consumable inputs, orchestrating multi-step Agentic workflows across the SDLC, and setting the standard for reviewing Agent-generated code and PRs with production-grade rigor.

• Experience shipping and operating agent-based systems in production at scale, across multiple teams or products, including evals, observability, and debugging of non-deterministic behavior.

• Experience defining the evals and observability strategy for non-deterministic systems and driving its adoption across teams.

• Contributions to the safety posture of AI systems, including prompt-injection defences, threat modeling, and audit trails.

• Experience owning and driving the architecture, design, and delivery of entire products or solutions that are deeply complex and often ambiguous, with measurable impact well beyond a single team.

• Cross-organization and company-wide influence: you build consensus among senior leaders and Principal/Partner engineers, resolve deep ambiguity, and drive large, complex initiatives to outcomes.

• Technical leadership: you set technical direction across multiple teams or an organization, mentor senior and Principal engineers, and establish engineering standards and best practices that others adopt.

• Experience architecting and operating company-scale, mission-critical distributed systems with stringent reliability, security, privacy, and compliance requirements.

• Industry or company-wide thought leadership in AI engineering—for example, driving adoption of emerging technologies, representing engineering to executives and external audiences, or contributions such as patents, publications, or widely adopted internal frameworks.