AI Deployment Strategist - US Enterprise
Enterprise AI / Strategy · 2–4 years · Full-time · Korea · Open
About EBIT
EBIT started as an AI data company specializing in building, validating, and operating the high-quality training data that AI model development requires, and is now an AI solutions company expanding into Enterprise AI, Global GTM, and Physical AI.
EBIT provides an integrated data build service covering the entire process from data design to collection, processing, and quality control, along with custom data solutions optimized to each client's requirements. Since 2024 we have worked with big tech companies in Korea and abroad on high-complexity domain data projects across language, coding, STEM, and finance, establishing ourselves as an APAC partner. We are now growing fast, expanding into Enterprise AI solutions for corporate clients, Global GTM, and Physical AI areas such as robotics and autonomous driving.
About the Role
The US AI Deployment Strategist is based at our Korea headquarters and dedicated to US enterprise clients. You work directly with US companies to analyze their business goals, workflows, and key pain points, and to identify opportunities where AI can improve or automate work or create new value.
Based on those opportunities, you define business requirements, data requirements, operating processes, and KPIs to design executable AI projects, and then lead the whole project through to deployment and operation of the client's AI solution.
Responsibilities
Define the client's AI vision and AI transformation strategy
Analyze the business goals, working environment, and AI maturity of US enterprise clients to identify adoption opportunities. Prioritize AI use cases by business value and feasibility, and build the AI transformation roadmap and phased execution plan.
Analyze client workflows and identify AI and AI Agent use cases
Analyze the client's core workflows and pain points to find use cases that AI and AI Agents can improve or automate. Turn them into concrete, executable tasks with business requirements, data requirements, operating processes, and KPIs.
Define AI solution adoption strategy and execution plan
Design how the identified use cases are applied in the client's real enterprise environment, taking into account AI Agents, the client's business systems, data platforms, and existing IT landscape. Work with the client and our internal engineering teams to define requirements and success criteria from PoC/Pilot to production deployment, and support execution.
Lead project collaboration with client and internal engineering, data, and IT teams (PM/PMO)
Own the core communication between the client and EBIT's engineering, data, and operations teams and lead the project end to end. Manage goals, schedule, key deliverables, and risks so that AI adoption turns into real business results.
Requirements
- Education: Bachelor's degree or higher (any major; business, economics, industrial engineering, computer science, statistics, or information systems preferred)
- Experience: 2+ years of global enterprise project experience in strategy, DT, or IT consulting, data/AI planning, digital strategy, new business, or IT planning
- (Required) Project and communication skills: English proficiency sufficient to independently run meetings, workshops, presentations, and day-to-day communication with US enterprise clients
- Understanding of digital, data, and AI technology: basic concepts of machine learning and LLMs, and an understanding of generative AI, RAG, AI Agents, and how such services are structured
- Strategy and planning: ability to analyze a client's business and workflows, structure problems, derive AI use cases, prioritize them, and build an execution roadmap
Preferred
- Direct collaboration experience with US enterprise clients or global cross-functional teams
- Experience delivering AI, data, or digital transformation consulting or B2B technology projects for US enterprise clients
- Experience planning, building, or operating enterprise AI services using LLMs, generative AI, RAG, AI Agents, MCP, or workflow automation
- Understanding of or project experience with LLMs, generative AI, RAG, AI Agents, MCP, workflow automation, model evaluation, and data pipelines
- Experience with business process innovation (BPR/PI) or AI governance driven by AI adoption
- Hands-on experience with Python or SQL for analysis or prototyping, and with cloud platforms (AWS, Azure, GCP)
