AI Implementation Strategist

Netex Consulting; Georgia; On-site; USD 100,000 - 130,000; Full time

Job summary

Netex Consulting in Georgia is seeking a Senior AI Implementation Strategist. This role focuses on designing and prototyping AI-driven workflows for real business operations, not just theoretical solutions. Candidates should have strong technical implementation skills, experience with automation tools, and the capability to document processes and workflows effectively.

The ideal candidate will actively collaborate with developers and technical teams, ensuring clear communication and ownership throughout the implementation process. If you're detail-oriented and have a passion for practical AI systems, we want to hear from you!

Qualifications

• Senior-level experience in technical implementation or automation roles.
• Hands-on experience with Cursor or Zed.
• Strong familiarity with structured Markdown documentation.

Responsibilities

• Design and implement AI-assisted workflows for business operations.
• Work hands-on with AI coding environments like Cursor or Zed.
• Document decisions, workflows, and technical requirements clearly.

Skills

Technical implementation

Automation

AI tooling

Strong analytical thinking

Excellent written communication

Collaboration with technical teams

Tools

Cursor

Zed

Git

GitHub/GitLab

Job description

Details

We are looking for a senior, technically strong AI Implementation Strategist to design, prototype, and deploy AI-driven workflows in real operational environments. This is not a general “AI consultant” role. We are looking for someone who can work hands‑on with AI coding tools, structured documentation, automation logic, prompts, workflows, APIs, and technical teams — and who can turn complex requirements into working AI-assisted systems.

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.