Director, AI Enterprise Architect

Locus Robotics

1d•$175,000 - $250,000

About The Position

Locus Robotics is a global leader in Physical AI for warehouse automation. We are at an inflection point: AI is moving from a productivity tool to the operating system of how we work and how our products deliver value. The Director, AI Enterprise Architect is the person who makes that transition real. 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 US at the frontier of Physical AI - at exactly the moment when the internal AI transformation is in flight. This AI-first transformation agenda has full CEO and board visibility. You will own the agenda, set the direction, and drive measurable impact that will enhance the capabilities of the global organization. This is a strategy and build role. You will own the roadmap, the infrastructure, and the execution.

Requirements

Demonstrated expertise across modern deep learning paradigms, with a strong focus on transformer-based and multimodal systems. Deep understanding of model architecture and adaptation techniques, including pretraining and fine-tuning strategies, RLHF/DPO, retrieval-augmented generation (RAG), embeddings and tokenization, parameter-efficient tuning (e.g., LoRA), and real-world LLM application design.
Established track record of designing, delivering, and operating production-grade AI systems at scale, with clear, measurable impact on business KPIs such as efficiency, revenue growth, cost optimization, or user experience.
Demonstrated ability to seamlessly integrate AI capabilities into enterprise systems of record, including CRM platforms, asset management systems, collaboration tools, and knowledge management ecosystems that drive end-to-end workflow transformation.
Working knowledge of data and dataset engineering, including data governance, classification, and security/compliance frameworks, as well as data architecture, integration pipelines, metadata management (catalogs, lineage), and modern data storage paradigms.
Proven experience architecting and delivering reusable, enterprise-grade AI platforms rather than isolated solutions. This includes development of shared infrastructure such as connectors, agent frameworks, MCP-style gateways, evaluation and observability tooling, and scalable deployment pipelines that enable organization-wide adoption.
Senior and detailed-level technical depth with the ability to perceive, architect, plan, and actively develop critical AI enterprise systems. Capable of rapid prototyping of core components and ideas and transforming ideas into deployed systems.
Outstanding ability to translate complex AI concepts into clear business value, engage and influence stakeholders across technical and non-technical functions, and drive alignment and execution in delivering transformative AI initiatives.
5+ years’ experience driving enterprise-wide AI transformation.
5+ years in software engineering, data engineering, or AI/ML roles.
Software engineering proficiency in Python. Comfortable with cloud infrastructure (AWS, Azure, or GCP) and production deployment.
Data engineering fundamentals - ETL/ELT pipelines, Lakehouse architecture, API integration, and data quality frameworks. Hands-on experience with Databricks or equivalent platforms.
Hands-on experience building and deploying agentic AI workflows in production - autonomous systems that take actions, not just generate outputs.
Deep proficiency with LLM integration, RAG pipeline design, prompt engineering, and tool use. Experience with Claude, GPT-4, or equivalent foundation models in enterprise contexts.
Classical ML skills across regression, classification, anomaly detection, and time-series forecasting.
Strong business acumen - able to extract the real problem behind the stated problem and build the solution in close collaboration with functional stakeholders.
Clear communicator. Able to present technical architecture to engineers and business impact to a C-level in the same conversation.
Proficient English communication skills, both written and verbal, with the ability to engage diverse audiences effectively.

Responsibilities

Define the prioritization framework, sequence the work, and ensure executive alignment across the company’s portfolio of AI-first transformation initiatives. Hold the organization accountable to business outcomes, not tool deployments.
Partner with functional leaders across Sales, CS, Finance, Operations, and Marketing to translate business requirements into AI-native workflows — redesigning processes from the ground up.
Design, build, and deploy AI agents that replace manual processes end-to-end. Own the full lifecycle from scoping through deployment and iteration. Production systems, not prototypes.
Connect disparate data sources into a unified context layer that makes foundation models more powerful and domain specific.
Ability to deploy machine learning techniques for more advanced use cases that require more sophisticated prediction, scoring, or forecasting.
Design and build cross-system data flows and the infrastructure that makes AI agents and models consumable across the enterprise.
Define tooling standards, model risk frameworks, data access policies, and security guardrails.
Champion AI-native ways of working across the organization. Mentor function leads.