Executive Development Manager - AI Engineering

Overview

We are seeking an AI Engineer to lead the engineering and productionization of agentic AI capabilities to be built on our enterprise AI Backbone across cloud and edge environments. This role owns the end-to-end delivery of production-grade AI agents, evaluation and regression quality gates, and MCP-based integrations with enterprise systems and data products. You will operate as a technical anchor—turning ambiguous workflows into measurable, reliable agent outcomes, proactively identifying risks and tradeoffs, and mentoring engineers to raise delivery and quality standards.

Responsibilities

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.

Qualifications

Key Skills/Experience Required Minimum Qualifications:
Minimum Qualifications
Bachelor’s/Master’s in CS/AI/ML/Data Science (or equivalent experience).
Demonstrated experience building and shipping AI/LLM-enabled solutions with production-quality engineering.
Required Expertise
Python + software engineering: clean architecture, tests, packaging, CI-friendly code, performance tuning
Agentic AI patterns: tool/function calling, planning/execution loops, failure recovery, prompt/system instruction design and versioning
Evaluation discipline: offline eval, regression suites, golden sets/labels, metrics selection, experiment reporting
Integration engineering: APIs, auth concepts, schema-driven tool integration; MCP servers/clients preferred
Observability: structured logging, correlation IDs, latency/error instrumentation, debugging production issues
Collaboration/influence: requirements shaping, stakeholder alignment, mentoring L08 engineers
Differentiating Competencies
Ownership: delivers outcomes across a workflow/workstream, not just tasks; drives closure and quality
Collaboration & customer focus: builds solutions that improve business workflows; manages stakeholder expectations
Communication: clear technical narratives, evidence-based recommendations, crisp updates on risks and progress
Adaptability: adjusts to model/tool/platform constraints quickly without quality regression
Proactiveness & initiative: anticipates dependencies and proposes options early
Strategic thinking (emerging): identifies reusable patterns and sequencing that accelerate cross-domain adoption