Sr. Director of AI Infrastructure

ARMRA Colostrum

3d•Remote

About The Position

ARMRA is seeking a Sr. Director of AI Infrastructure to own, build, and scale the foundational AI infrastructure across the entire organization. As ARMRA's dedicated AI leader, you will be the single owner of technical standards, governance, and the scalable architecture that connects all of ARMRA's business systems into a unified intelligent automation layer — partnering cross-functionally with the Director of IT as needed to ensure alignment across the broader technology ecosystem. This is a critical, high-visibility, company-wide role. ARMRA has established a strong foundational infrastructure connecting platforms across customer experience, e-commerce, retail, marketing, finance, and operations. You will inherit that foundation and take full ownership of it — driving improvements, closing gaps, implementing new capabilities, and expanding the infrastructure as the business evolves — while enabling AI agents to take real action on real data in real time. This role is the definitive bridge between leadership's strategic vision and the enterprise-grade execution required to sustain and advance a company-wide AI backbone. You will own and continuously develop a uniform, resilient, and high-performance AI playbook that serves every function of the business equally — from Retail and Operations to Marketing, Finance, and Customer Experience. The right person for this role has built, owned, and scaled production integration systems end-to-end. You understand that the best AI architecture is not about training models — it's about connecting systems, enforcing governance, and making intelligent automation reliable enough to trust across every surface of the business. Whether you come in at the Director or Senior Director level, you bring deep technical ownership, sound strategic judgment, and the ability to move with urgency in a fast-paced environment.

Requirements

8–12+ years in infrastructure engineering, solutions architecture, or a principal/lead technical role, with direct experience building and scaling enterprise-scale integration systems in CPG, consumer brands, or omnichannel retail environments. Candidates with deeper experience will be considered for the Senior Director level.
Strong hands-on experience with AWS (required). Experience architecting and managing cloud-native environments for production API services, not just dev/test workloads.
Deep experience with REST APIs, webhooks, middleware design, and data pipeline architecture. You've connected messy real-world business systems across multiple departments, not just built demos.
Direct experience deploying and managing LLM-powered systems in production (API integrations, prompt management, agentic workflows). You don't need to train models — you need to make them work reliably at scale.
Experience building and maintaining business intelligence infrastructure: data warehousing, unified reporting, and cross-departmental analytics.
Strong understanding of data governance, privacy compliance (CCPA, GDPR), and security best practices for handling customer PII across integrated systems.
Exceptional ability to lead complex technical initiatives with cross-departmental impact, requiring alignment from multiple executive stakeholders.
Ability to translate between business stakeholders and technical execution. You can sit in a room with a VP and a developer and make sure both walk out aligned.
Strong documentation habits. If you build it, you document it. If you change it, you update the docs.
Comfortable working autonomously in a fast-paced, remote environment where priorities shift and you're expected to move without waiting for permission.
Bias toward building and shipping over planning and presenting. We need someone who gets things into production, not someone who makes great slide decks about what could be.

Nice To Haves

Hands-on experience with LLM APIs and building agentic AI systems in production.
Background in CX platforms, e-commerce infrastructure, or retail technology systems.
Experience with containerization (Docker) and orchestration for deploying AI microservices.
Familiarity with emerging AI integration standards and protocols.
Experience with data warehousing platforms and modern ETL/ELT tools.
Prior experience building internal tools or dashboards for non-technical users.

Responsibilities

Own and continuously develop the integration architecture connecting ARMRA's core business platforms across e-commerce, retail, marketing, finance, operations, and customer experience into unified, queryable data flows.
Build, maintain, and extend middleware and API integrations that enable AI agents to perform real actions across business functions: modifying orders, managing subscriptions, surfacing inventory, pulling financial data, and automating operational workflows — all gated to appropriate system states with hard fail safeguards.
Assess, enhance, and expand the existing centralized data architecture to ensure it remains optimized for real-time inference, business intelligence, unified reporting, and AI-driven decision-making as business needs evolve.
Establish and enforce enterprise-wide standards for API security, latency, and throughput across all integrated systems.
Own pipeline reliability, monitoring, and documentation. When something breaks, you fix it. When something's fragile, you rebuild it.
Evaluate and recommend new tools, connectors, and platforms based on API capability, data governance requirements, and integration feasibility.
Architect, deploy, and optimize AWS cloud environments for performance, reliability, cost efficiency, and scalability across the full AI ecosystem.
Deploy and manage production AI systems — primarily LLM API integrations, workflow automation, and agentic AI agents — with appropriate monitoring, guardrails, and escalation paths.
Build and extend agentic AI systems that execute multi-step tasks autonomously across departments with clear boundaries and human handoff protocols.
Own prompt architecture at the infrastructure level: API configuration, system prompt management, version control, and testing frameworks for production AI workflows.
Drive optimization of cloud computing resources and API usage across the entire AI ecosystem to ensure cost efficiency, elasticity, and scalability.
Ensure all AI implementations and data integrations comply with ARMRA's privacy policy, data security SOPs, and regulatory requirements (CCPA/CPRA, GDPR where applicable).
Enforce data sovereignty across the enterprise: ARMRA's customer and business data stays on ARMRA's infrastructure. No PII traverses third-party rails without explicit governance approval.
Maintain and update the subprocessor inventory as new tools and integrations come online.
Partner with Legal and Compliance on AI-specific governance: customer disclosure requirements, model training prohibitions, vendor data protection standards, and PII handling protocols.
Own the technical side of data retention enforcement, deletion workflows, and audit trails across integrated systems.
Build and evolve the AI governance framework in partnership with Compliance, including approved tool lists, subprocessor documentation, and model training restrictions.
Serve as the primary technical consultant for department heads and executive leadership on AI architecture, investment trade-offs, and long-term capability planning.
Partner with leaders across all departments — Operations, Marketing, Finance, Retail, and Customer Experience — to identify high-impact automation opportunities and translate business needs into technical requirements.
Collaborate closely with the Director of IT to ensure AI infrastructure decisions are aligned with the broader technology ecosystem and organizational standards.
Run discovery sessions with stakeholders to map current workflows, quantify manual effort, and prioritize builds based on ROI and feasibility.
Build and maintain dashboards and reporting infrastructure that give leadership visibility into AI performance, automation coverage, and operational efficiency gains across the business.
Stay current on emerging AI tools, APIs, and integration patterns. Bring recommendations with a plan, not just awareness. If something can make us faster, flag it with a build timeline.