AI Deployment Strategist - Korea Enterprise

Enterprise AI / Strategy · 2–4 years · Full-time · Seoul · 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 Korea AI Deployment Strategist works directly with Korean enterprise clients 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

Identify AI adoption opportunities based on the client's business goals and current state. Build the AI adoption roadmap, prioritize use cases, and set out the business case and a phased execution plan.

Analyze client workflows and identify AI Agent use cases

Analyze the client's workflows and pain points to find tasks that AI and AI Agents can improve or automate. Turn them into concrete, executable tasks with business requirements, data requirements, operating processes, and KPIs.

Deliver AI solution adoption projects

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 in strategy, DT, or IT consulting, data/AI planning, digital strategy, new business, or IT planning
  • 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
  • Project and communication skills: ability to define requirements and coordinate projects with clients and engineering, data, and IT stakeholders

Preferred

  • Experience in AI, data, or AX/DT consulting or B2B projects for enterprise clients
  • Experience planning, piloting, or operating LLM or ML-based services, or related MLOps/AIOps projects
  • 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)