AI Implementation Strategist

Required Skills

Must-have requirements

• Senior-level experience in technical implementation, automation, AI tooling, software-adjacent roles, product engineering, solution architecture, or technical project delivery.
• Hands-on experience with Cursor or Zed.
• Strong familiarity with .md files, structured Markdown documentation, repositories, specifications, prompts, and technical notes.
• Solid understanding of LLMs, prompt engineering, AI-assisted development, AI agents, workflow automation, and API-based integrations.
• Ability to read and understand technical documentation, implementation logic, and developer workflows.
• Strong analytical thinking with the ability to break down complex systems into clear implementation steps.
• Excellent written communication skills for creating technical documentation.
• Comfortable collaborating directly with technical teams and challenging implementation assumptions.
• High level of ownership, precision, and the ability to work independently.

Nice to have

• Experience with Git, GitHub/GitLab, JSON, YAML, REST APIs, webhooks, automation platforms, or low-code/no-code tools.
• Experience with customer support automation, e-commerce systems, CRM tools, chatbots, voicebots, or AI agents.
• Basic understanding of frontend and backend architecture.
• Experience creating internal documentation, SOPs, prompt libraries, or AI governance documentation.

About the role

This role is ideal for someone who is senior enough to understand the full implementation picture, technical enough to work close to the code, and structured enough to document everything clearly.

We are building practical AI systems that are used in production—not slide decks or abstract strategy.

Responsibilities

What you’ll do

• Design and implement AI-assisted workflows for real business operations.
• Work hands-on with AI coding environments such as Cursor or Zed.
• Create, edit, and maintain structured .md documentation, prompt libraries, system instructions, implementation specifications, and workflow definitions.
• Translate operational problems into technical AI implementation plans.
• Prototype AI assistants, automation flows, internal tools, and agentic workflows.
• Collaborate closely with developers, product owners, system architects, and operational teams.
• Define implementation logic, data flows, fallback behavior, escalation rules, and quality-control mechanisms.
• Test, debug, and refine AI outputs, prompts, automations, and integrations.
• Document decisions, workflows, edge cases, and technical requirements clearly and consistently.
• Support deployment, monitoring, iteration, and the long-term maintainability of AI solutions.