Executive Development Manager - AI Engineering

1) Agent & AI Solution Engineering (35%)

• Lead development and productionization of agent modules and solution components (planning/tool-use, constraints, recovery behaviors) aligned to workflow outcomes. (Execute)
• Translate ambiguous requirements into clear acceptance criteria, testable behaviors, and delivery plans drive execution with partner teams. (Consult/Execute)
• Optimize reliability and performance (latency, token efficiency, tool success) through systematic tuning and failure-mode remediation. (Execute)

2) Evaluation, Testing & Quality Governance (25%)

• Define evaluation approach for assigned workflows: test sets, golden labels (where applicable), metric thresholds, and regression strategy. (Execute/Consult)
• Implement and maintain evaluation pipelines/harnesses generate evidence-based comparisons of models/prompts/agent variants. (Execute)
• Drive root cause analysis for failures (hallucination/tool errors/routing failures) and implement corrective actions reduce defect leakage. (Execute)

3) Model/Prompt Routing Enablement (15%)

• Contribute to model routing logic where applicable: implement routing rules/classifiers, validate decision quality/latency, and ensure auditability via metadata/logging. (Execute/Consult)
• Proactively recommend routing adjustments based on measured performance/constraints (within governance boundaries). (Consult)

4) Integration with Tools and MCPs (15%)

• Lead implementation of MCP connectors/clients for enterprise systems and internal data products, ensuring schema/version discipline, correct scopes, and reliable integration tests. (Execute/Consult)
• Create reusable integration patterns, documentation, and scaffolding to accelerate future connectors. (Execute)

5) Operational Readiness & Cross-Team Delivery (10%)

• Ensure production readiness: telemetry coverage, runbooks/rollback notes, basic SLI/SLO alignment for owned components support incident triage and stabilization. (Execute/Consult)
• Proactively identify risks/dependencies (security scopes, data policy, platform constraints), propose options/tradeoffs, and drive resolution. (Consult)

Decision-Making Autonomy: High-moderate - significant autonomy in solution design, evaluation approach, and integration implementation escalates policy/security-impacting decisions.
Supervision Required: Moderate-low - operates with general direction from L10+ lead/architect periodic design and release reviews.
Complexity of Role: High - multi-system integration, measurable AI quality, production reliability, and cross-team delivery under evolving requirements.
Cross-Functional Interactions: Yes - continuous interaction with product/domain teams, platform/SRE, security/compliance, and enterprise app owners.