Agentic Ai Engineer by Shaik Ahmed

Agentic Ai Engineer by Shaik AhmedAgentic Ai Engineer by Shaik Ahmed

Agentic Ai EngineerShaik Ahmed

I design and build end-to-end agentic AI systems using n8n, LangChain, and Python that can reason, retrieve knowledge, and act autonomously across tools, APIs, and databases. I specialize in production-ready AI automations with vector databases, RAG pipelines, and robust error handling—built for real business workflows, not demos. My focus is reliability, scalability, and clean handover so teams can confidently run and extend the system.

What's included

End-to-End Agentic AI Workflow Design & Architecture

Description Design and implement a complete agent-based AI system tailored to your business use case (customer support, data processing, lead enrichment, document handling, etc.). Includes Multi-agent architecture (planner, executor, validator agents) Tool calling & decision logic Error handling & fallback strategies Clear workflow diagram Tools & Tech n8n / LangChain / Python Gemini / OpenAI / Claude / local LLMs REST APIs & Webhooks Format n8n workflow JSON Architecture diagram (PNG / PDF) Documentation (Notion / PDF) Revisions Up to 2 revisions

AI Automation Workflows & Integrations (Production-Ready)

Description Build reliable, production-grade AI automations that integrate seamlessly with your business tools. Includes Trigger-based automations (email, webhook, CRM, forms) AI-driven decision & reasoning layers API integrations (Slack, Gmail, Google Sheets, CRMs, internal tools) Logging, retries, monitoring & observability Security & access control best practices Tools n8n, Python services External & internal APIs Deliverables Fully configured n8n workflows Integration setup & configuration Usage & maintenance guide Revisions Up to 2 revisions

Knowledge, Memory & Database Layer Implementation

Description Implement a scalable knowledge and memory system enabling agents to retrieve, learn, and reason over structured and unstructured data. Includes Vector database setup for semantic search Long-term & short-term agent memory Embedding pipelines Hybrid storage (vector + traditional databases) Self-hosted or cloud-based deployment Databases Vector DBs: Pinecone, FAISS, Chroma, Weaviate Traditional DBs: PostgreSQL, MySQL, MongoDB Deliverables Database schema & configurations Python ingestion & retrieval scripts RAG & memory documentation

Custom Python AI Tools & Services

Description Develop custom Python components that extend agent capabilities beyond no-code limitations. Includes Custom LangChain tools & agents PDF, image & document extraction pipelines Background services for AI agents Cost & performance optimized prompts Optional Dockerized deployment Deliverables Python source code Environment & deployment instructions API endpoints (if applicable) Revisions Up to 1 major revision

Testing, Optimization & Production Deployment

Description Ensure the AI system is reliable, secure, and optimized for real-world usage. Includes Agent behavior & edge-case testing Performance & cost optimization Prompt tuning & evaluation Monitoring, logs & alerting Production deployment support Deliverables Test cases & validation report Optimized workflows Deployment checklist

Documentation, Handover & Support

Description Provide complete documentation and knowledge transfer to ensure long-term maintainability. Includes Step-by-step setup & usage documentation Architecture explanation Optional live walkthrough or recorded session Post-delivery support window Deliverables Notion / PDF documentation Recorded walkthrough (optional)

FAQs