Job Title: Process Specialist (AI Context Engineer) / Proofreader
Purpose:
The Process Specialist (AI Context Engineer) / Proofreader is responsible for documenting, optimizing, and translating customer journeys and internal workflows into AI-ready process formats. This supports automation, SmartAgent training, and operational transformation across clients. The role also includes proofreading other process specialist guides to ensure accuracy, consistency, and adherence to standards.
Key Responsibilities:
- Map existing customer and operational processes into standardized AI Journey Templates.
- Identify friction points, process gaps, and opportunities for automation.
- Collaborate with AI Builders and Engineers to define handlers, routes, and data payload requirements.
- Ensure consistency and compliance across all process documentation, SOPs, and knowledge bases.
- Conduct validation sessions with Operations, QA, and clients to ensure process accuracy.
- Proofread other process specialists’ work to ensure SOP adherence and quality.
- Maintain trackers, logs, and change records for transparency and audit readiness.
- Support Co-QA sessions with clients to align process accuracy with policy and operational standards.
Core Skills:
- Strong analytical and process mapping expertise.
- Proficiency in visual documentation tools (Figma, Lucidchart, Miro, Draw.io).
- Proficient with Google Workspace (Docs, Sheets, Slides).
- Basic understanding of API logic, inputs/outputs, and workflow automation triggers.
- Excellent written communication skills, able to simplify complex workflows into clear, step-by-step instructions.
- High attention to detail and organizational discipline in version control and documentation structure.
Preferred Background:
- 2+ years of experience in Process Improvement, Business Analysis, or CX Workflow Design.
- Experience in AI Automation, BPO Operations, or Digital Transformation initiatives.
- Familiarity with AI Ops, conversational AI frameworks, or SmartAgent training.
- Knowledge of RAG workflows, Agentic RAG, Traditional RAG, multi-agent systems, and knowledge-augmented generation (KAG).