Director, Commercial AI Orchestration Architect

• Own the end-to-end architecture of the agentic orchestration capability, including experience composition, cross-channel optimization, journey generation, execution, feedback, and continuous learning.
• Define how orchestration consumes customer signals, barrier insights, brand priorities, approved content, channel eligibility, consent, suppression rules, and engagement history.
• Establish clear boundaries and interfaces between insight generation, decisioning, journey orchestration, channel execution, and outcome measurement.
• Define the architecture of the agents, their responsibilities, tool access, decision rights, escalation paths, and interaction patterns.
• Design the orchestration solution to support field calls, remote engagement, approved email, events, headquarters email, and third-party channels.
• Translate commercial objectives and brand guardrails into configurable orchestration rules and testable agent behaviors.
• Define technical acceptance criteria and the definition of done for all major engineering deliverables.
• Validate critical design decisions through code reviews, prototypes, technical walkthroughs, test evidence, and production-readiness assessments.
• Ensure every recommendation and action is explainable, traceable, compliant, and reproducible.
• Establish evaluation methods for journey quality, recommendation relevance, guardrail adherence, execution success, and measurable business outcomes.
• Define the architecture for human review, exception handling, approval, escalation, and safe fallback when an agent cannot act confidently.
• Ensure orchestration logic, source code, configurations, documentation, and operational knowledge are transferred to and owned by BMS.
• Proven experience designing and delivering production-grade next-best-action, next-best-engagement, journey optimization, decisioning, or omnichannel orchestration platforms.
• Hands-on experience with agent frameworks such as LangGraph, Semantic Kernel, AutoGen, LangChain, or comparable orchestration frameworks.
• Experience designing agent tool access using Model Context Protocol (MCP), APIs, event-driven integration, webhooks, and asynchronous messaging patterns.
• Strong knowledge of context engineering, prompt management, structured outputs, tool calling, grounding, retrieval-augmented generation, and agent memory patterns.
• Experience defining and managing customer and journey state across multiple agents, interactions, channels, and execution cycles.
• Ability to design rules engines, constraint-based decisioning, prioritization logic, optimization models, and AI-driven decisioning within a common orchestration architecture.
• Understanding of decisioning methods such as contextual bandits, reinforcement learning, predictive scoring, mathematical optimization, and hybrid rules-and-model approaches.
• Strong understanding of closed-loop orchestration, including execution acknowledgements, response signals, outcome measurement, feedback loops, model evaluation, and continuous optimization.
• Experience designing explainable and auditable decisioning, including decision traces, reason codes, model and prompt versioning, input lineage, configuration history, and reproducibility.
• Knowledge of agent evaluation frameworks, including offline evaluation, simulation, regression testing, adversarial testing, guardrail testing, journey-quality measures, and production monitoring.
• Experience establishing LLMOps and MLOps practices, including CI/CD, model and prompt lifecycle management, evaluation gates, environment promotion, observability, rollback, and incident management.
• Strong understanding of production AI concerns such as latency, token usage, cost controls, caching, concurrency, rate limits, retries, idempotency, circuit breakers, and graceful degradation.

None flagged.