The Senior Director, AI Architecture & Platform Engineering will define, build, and scale Acosta Group’s enterprise AI architecture and platform engineering capability. This leader will own the technical foundation for AI solutions, agents, copilots, intelligent workflows, model lifecycle management, observability, integration, reusable services, and secure platform operations.
The role supports the AI CoE operating model of central guardrails, federated execution, and portfolio-driven scaling by ensuring AI solutions are built as reusable, governed, production-ready enterprise capabilities rather than disconnected pilots or vendor-specific implementations.
- Define and own Acosta Group’s enterprise AI reference architecture across traditional AI, GenAI, agentic AI, copilots, RAG, orchestration, workflow automation, and AI-enabled enterprise applications.
- Build and scale enterprise AI platform engineering capabilities, including reusable services, shared APIs, integration frameworks, orchestration patterns, developer tooling, templates, and production-grade platform components used across multiple teams and business domains.
- Lead the design and delivery of production AI solutions at scale, including GenAI applications, agentic workflows, AI copilots, intelligent automation, and enterprise AI products that move beyond pilots and proofs of concept.
- Establish enterprise engineering standards, platform guardrails, reference implementations, reusable accelerators, and developer-ready patterns that enable secure, scalable, interoperable, and cost-effective AI delivery.
- Define and mature MLOps, LLMOps, ModelOps, observability, evaluation, lifecycle management, platform reliability, security, DevSecOps, CI/CD, and production support practices for enterprise AI systems.
- Guide the use of modern AI, cloud, and data ecosystems including Azure, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Kubernetes, APIs, and enterprise integration patterns to support reusable and governed AI capabilities.
- Provide senior technical leadership to transformation programs, business-unit AI initiatives, internal engineering teams, and delivery partners to ensure reuse, interoperability, scalability, security, production readiness, and alignment with the AI CoE operating model.
- Influence executive and senior technology stakeholders by evaluating emerging technologies, challenging architectural decisions, and making transparent trade-offs across scalability, risk, performance, cost, speed, and business value.
- Partner with AI Governance, Cybersecurity, Data, Technology, Responsible AI, and business leaders to embed governance, security, compliance, responsible AI controls, and cost optimization directly into AI engineering workflows and platform services.
- 15 or more years of progressive technology leadership experience across software engineering, platform engineering, cloud, data, AI/ML, digital products, or enterprise technology organizations.
- 7 or more years leading large-scale engineering teams responsible for platform engineering, AI/ML engineering, cloud platforms, data platforms, enterprise software products, or related production technology capabilities.
- Proven experience building and scaling enterprise platforms including reusable services, shared capabilities, APIs, integration frameworks, developer tooling, and production-grade systems used across multiple teams or business domains.
- Hands-on experience delivering production AI solutions at scale, including Generative AI, agentic AI, AI copilots, intelligent automation, and enterprise AI applications beyond pilots and proofs of concept.
- Deep technical expertise in AI architecture, GenAI, agentic AI, RAG, orchestration frameworks, APIs, cloud-native architecture, MLOps/LLMOps, observability, platform reliability, security, and enterprise data platforms.
- Strong experience with modern AI and cloud ecosystems, including Azure, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Kubernetes, DevSecOps, CI/CD, and enterprise integration patterns.
- Proven ability to define enterprise AI reference architectures, engineering standards, and platform guardrails that enable scalability, security, reuse, interoperability, cost optimization, and production readiness across the organization.
- Executive-level technical leadership skills, with the ability to influence senior leaders, challenge architectural decisions, evaluate emerging technologies, and make complex trade-offs across scalability, risk, performance, cost, and business value.
- Exceptional communication skills, with the ability to translate complex technical concepts into clear business outcomes, strategic decisions, and enterprise transformation impact.
- Preferred experience building enterprise AI, cloud, or data platforms within Fortune 500, technology, SaaS, hyperscaler, or AI-native organizations.
- Preferred experience leading enterprise-scale AI transformation initiatives that balance centralized governance with federated delivery and adoption.
Work Environment and Physical Requirements
The work environment characteristics described are representative of those an employee may encounter while performing the essential functions of this job. Job may require moderate physical effort including lifting materials and equipment weighing less than 15 pounds. This position involves viewing a computer monitor for more than 30% of the time. Personal protective equipment may need to be worn. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.