Director, AI Enterprise Architect

Responsibilities

Enterprise AI Transformation Strategy: Define and drive the company-wide AI roadmap, including prioritization frameworks, sequencing of initiatives, and executive alignment. Ensure a relentless focus on business outcomes rather than tool adoption.

AI-Native Workflow Redesign: Partner with leaders across Sales, Customer Success, Finance, Operations, and Marketing to identify high-leverage opportunities. Redesign processes from the ground up into AI-native, automated workflows.

AI Systems & Agentic Workflow Development: Design, build, and deploy production-grade AI systems, including agentic workflows that automate end-to-end processes. Own the full lifecycle—from scoping through deployment, monitoring, and iteration.

LLM & Data Integration Architecture: Architect scalable LLM-powered systems, including retrieval-augmented generation (RAG), unified context layers, and integration frameworks that connect enterprise data sources.

Data & Platform Engineering: Design and implement robust data pipelines, integration layers, and shared infrastructure that enable reusable, enterprise-wide AI capabilities. Ensure reliability, scalability, and accessibility across systems.

AI Governance, Security & Standards: Establish frameworks for model governance, risk management, data access, and security. Define standards for tools, evaluation, and responsible AI usage.

Technical Leadership & Culture Building: Drive AI adoption across the organization by mentoring leaders, establishing best practices, and fostering AI-native ways of working.

Core Expertise

• Deep expertise in modern AI techniques, including transformer architectures, multimodal systems, and LLM application design. Strong understanding of: Fine-tuning and adaptation (LoRA, PEFT, RLHF/DPO), RAG systems, embeddings, and tokenization and Prompt engineering and tool-augmented agents
• Proven track record designing and operating production-grade AI systems that deliver measurable business impact (e.g., efficiency, revenue growth, cost reduction, user experience).
• Experience embedding AI into core enterprise systems (CRM, ERP, knowledge systems, collaboration platforms) to enable end-to-end workflow transformation.
• Strong grounding in: Data engineering (ETL/ELT, pipelines, APIs), Data architecture (Lakehouse, storage systems), Metadata systems (catalogs, lineage), Governance, security, and compliance frameworks

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field required; Master’s or PhD preferred