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AI Architect

Rayomind Software Solutions LLC, DBA, Hire'in SolutionsNew York, NY🇺🇸United States

Why this role stands out

This AI Architect role offers a fantastic opportunity to shape enterprise-scale AI and Generative AI solutions, leveraging your expertise in LLMs, RAG, and cloud platforms. If you thrive on translating complex business needs into innovative, production-ready architectures within a dynamic hybrid environment, this position is perfect for you to make a significant impact. Apply now to join a forward-thinking team and advance your career in cutting-edge AI development.

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationNew York, NY, United States
MicroservicesAWSMLOpsAzureGenerative AILLMPython

Job description

Role:- AI Architect 

Location: New York, NY

Job Type: Contract

Job Description:

Experience: 12+ years overall, with 4+ years in AI/ML or Generative AI architecture

Client is seeking an experienced AI Architect to design, architect, and deliver enterprise-scale Artificial Intelligence and Generative AI solutions. The ideal candidate will have strong hands-on experience with LLMs, RAG, Agentic AI, cloud platforms, and AI application architecture, along with the ability to translate business requirements into secure, scalable, and production-ready solutions.

Key Responsibilities

  • Design end-to-end AI/ML and Generative AI architectures across application, data, and infrastructure layers.
  • Architect and implement LLM-based solutions, RAG pipelines, AI agents, and multi-agent orchestration frameworks.
  • Evaluate and integrate models and platforms such as Azure OpenAI, AWS Bedrock, Google Vertex AI, and other enterprise AI services.
  • Design AI solutions using Python, LangChain, LangGraph, vector databases, APIs, and microservices.
  • Establish AI governance, security, responsible AI practices, data privacy, and model-risk controls.
  • Guide engineering teams on MLOps/LLMOps, CI/CD, model deployment, monitoring, evaluation, and optimization.
  • Lead architecture reviews, technical discovery sessions, proof-of-concepts, and production implementation.
  • Collaborate with business stakeholders, product teams, data scientists, developers, DevOps, and security teams.
  • Define reusable AI reference architectures, design patterns, standards, and technical documentation.
  • Optimize AI solutions for scalability, latency, cost, reliability, and business value.

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