Director, AI Enterprise Architect

At Locus Robotics, we build AI-powered systems and intelligent robots that keep global supply chains running. Our platform combines advanced AI, real-time decision-making, and autonomous robotics to help leading companies improve efficiency, scale operations, and adapt to constant change. Locus Robotics is a place to do meaningful work with real-world impact, where your ideas move quickly from concept to deployment. We invest in our people, encourage continuous learning, and give you the opportunity to grow your career while building technology that is used every day at global scale. The Director, AI Enterprise Architect is a high-visibility, cross-functional leadership role with direct C-suite mandate. You will act as a force multiplier across the organization partnering with leaders to identify where AI creates the highest leverage, translating business requirements into scalable AI-native workflows, and building them.  This is a rare opportunity to join one of the largest privately held robotics companies in the U.S. at the frontier of Physical AI, at a moment when enterprise-wide AI transformation is actively underway. This AI-first agenda has full CEO and board visibility. You will own the direction, execution, and impact. This is both a strategy and build role - you will define the roadmap, architect the platform, and lead execution. 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 5+ years leading enterprise AI or digital transformation initiatives, with demonstrated ownership of strategy through execution 5+ years of hands-on experience in software engineering, data engineering, or AI/ML roles, with strong proficiency in Python and modern cloud platforms (AWS, Azure, or GCP) Proven experience designing and deploying production-grade AI systems, including agentic workflows that automate end-to-end processes and drive measurable business outcomes Deep expertise in LLM-based systems, including RAG architectures, prompt engineering, tool integration, and enterprise use of foundation models (e.g., GPT-4, Claude, or equivalent) Strong foundation in data engineering and architecture, including ETL/ELT pipelines, APIs, Lakehouse environments (e.g., Databricks), and data quality/governance frameworks Experience building scalable AI platforms including: Shared services, connectors, and agent frameworks, eEvaluation and observability tooling and deployment and scaling infrastructure Ability to operate at both