Process Analysis & Optimization: Evaluate cross-departmental processes, identify bottlenecks and manual/repetitive tasks, and deliver AI-driven solutions to enhance efficiency.
Intelligent Workflow Design: Build automated workflows and integrate systems/tools (e.g., Gmail, Google Workspace, CRM, ClickUp, databases, custom webhooks, etc.).
LLM & Agentic AI Solutions: Design and implement RAG architectures, multi-step AI agents, prompt engineering, structured outputs, and tool/function calling.
Technical Integration & Development: Work with REST APIs, webhooks, and JSON; develop custom scripts/services in Python when required.
End-to-End AI Lifecycle Management: Manage projects from requirements gathering and architecture design to testing, production deployment, stability evaluation, logging, and error handling.
Documentation & Training: Document workflows, APIs, and prompts thoroughly, while training and empowering internal teams to use AI tools safely and effectively.
Must-Have Qualifications & Skills
Experience: Minimum 2 years of relevant experience in AI, automation, software engineering, or process optimization.
Language: Advanced English proficiency (ability to read complex technical documentation and communicate effectively).
Programming & Web Standards: Intermediate-to-advanced proficiency in Python, REST APIs, webhooks, and JSON.
Automation Tools: Hands-on experience with n8n, Manus, Make, or Zapier.
Large Language Models (LLMs): Practical experience working with APIs from leading models (OpenAI, Claude, Gemini, Llama, etc.).
Modern AI Concepts: Experience building AI agents, RAG architectures, embeddings, and working with vector databases.
Databases & DevOps Basics: Familiarity with SQL, databases, Git, and basic Docker/Cloud concepts.
Soft Skills: Strong analytical and problem-solving mindset, excellent communication skills across technical and non-technical teams, and the ability to manage projects independently.
Nice-to-Have (Bonus Points)
Experience with frameworks like LangChain, LangGraph, LlamaIndex, or CrewAI.
Familiarity with multi-agent systems and the Model Context Protocol (MCP).
Hands-on experience with vector databases such as Qdrant, Pinecone, Milvus, FAISS, or pgvector.
Experience in AI evaluation, observability, guardrails, and AI system security.
Prior experience implementing AI solutions in service-oriented companies, e-commerce, tech firms, or multi-departmental organizations.