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AI Architect

Role overview

Qualifications

  • 12–15+ years of overall IT experience with strong architecture background.
  • Extensive hands-on experience with Python development.
  • Strong expertise in LangChain and LangGraph frameworks.
  • Hands-on experience with vector databases such as Pinecone, Weaviate, ChromaDB, or FAISS.

Responsibilities

  • Design enterprise-grade AI solutions.
  • Build scalable RAG (Retrieval-Augmented Generation) pipelines.
  • Integrate MCP servers using Python.
  • Develop production-ready AI applications leveraging modern LLM ecosystems.

Key facts

  • Remote from: Anywhere
  • Fixed term
  • Expert & Leadership (>10 years)
  • AI Solutions Architect
  • English

Hard skills

About the company

Workiy Inc. logo

Workiy Inc.

  • IT Services & Consulting
  • SME
  • 11–50
Workiy provides digital solutions and staffing services, utilizing our patented delivery model and a unique and flexible, client-centric approach to tackling technology's biggest challenges. We don’t have “clients” we have partners! Our mission is to enable your talented teams and amazing systems to assist your business in optimally achieving its goals. The Workiy team, brings to the table, more than 20+ years of experience in the technology industry across many verticals in both the private and public sector.

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Job description

This is a remote position.

We are seeking a highly experienced AI Architect with deep expertise in Generative AI, Large Language Models (LLMs), and modern AI orchestration frameworks such as LangChain and LangGraph. The ideal candidate will have strong hands-on experience designing enterprise-grade AI solutions, building scalable RAG (Retrieval-Augmented Generation) pipelines, integrating MCP servers using Python, and developing production-ready AI applications leveraging modern LLM ecosystems.



Requirements

  • 12–15+ years of overall IT experience with strong architecture background.
  • Extensive hands-on experience with Python development.
  • Strong expertise in LangChain and LangGraph frameworks.
  • Experience integrating MCP servers using Python.
  • Deep understanding of RAG architecture and LLM application development.
  • Hands-on experience with vector databases such as Pinecone, Weaviate, ChromaDB, or FAISS.
  • Experience working with OpenAI, Anthropic, Gemini, Llama, or other LLM ecosystems.
  • Strong understanding of prompt engineering and AI orchestration patterns.
  • Experience developing REST APIs and microservices.
  • Familiarity with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.
  • Experience with AI monitoring, evaluation, and optimization techniques.
  • Strong knowledge of scalable distributed systems and enterprise architecture.


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