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About the role
Owning the run‑state reliability of AI‑driven support workflows, including incident response, live‑site health, and continuous tuning. Adapting 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. Ensuring security, privacy, and responsible AI compliance, including rethinking role-based access control (RBAC), data access, case ownership vs. processing, and data exposure. Defining and implementing observability, monitoring, and intervention mechanisms for multiple AI agents operating concurrently. Partnering across engineering, support business, compliance, and platform teams to establish scalable patterns for AI‑managed support. Contributing to the vision and delivery of a platform that enables citizen developers to safely build AI agents for support workflows with reduced barrier to entry. Required Qualications Bachelor's degree in computer science or related technical field and 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python or equivalent experience. Other Qualificaitons Experience building LLM-powered applications, RAG pipelines, prompt engineering, agent frameworks (Semantic Kernel, LangChain), or fine-tuning with an eye for evaluation, latency, and cost. Proficiency 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 reviewing Agent-generated code and PRs with production-grade rigor. Experience shipping agent-based systems in production, including hands-on experience with evals, observability, and debugging. Experience standing up evals or observability for non-deterministic systems. Experience contributing to the safety posture of AI systems, including prompt-injection defences and audit trails. Ability to own and ship significant features or architectural components end to end. Collaboration across teams: experience aligning with partners and move work forward together.
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