Kavant Solutions

AI Architect

Remote, US$131,162-$180,002Posted 1 day ago

Job Description

Job Overview

We are seeking a dynamic and innovative AI Architect to lead the design, development, and implementation of cutting-edge artificial intelligence solutions across diverse enterprise environments. In this role, you will leverage your expertise in system architecture, cloud computing, and AI technologies to craft scalable, secure, and high-performance AI systems. Your leadership will drive the strategic integration of AI capabilities into our core products and services, fostering innovation and operational excellence. If you thrive in a fast-paced environment where creativity meets technical mastery, this opportunity is for you!

Responsibilities

  • Design and develop comprehensive AI architectures that align with business goals and technical requirements, ensuring scalability and robustness.
  • Lead the integration of AI models and algorithms into existing enterprise systems, utilizing RESTful APIs, microservices, and service-oriented architecture principles.
  • Collaborate with cross-functional teams to gather requirements, translate them into technical specifications, and develop proof-of-concept prototypes.
  • Oversee cloud-based deployment strategies utilizing platforms such as AWS, Azure, or Google Cloud to ensure optimal performance and security.
  • Implement DevOps practices including CI/CD pipelines using Jenkins, Docker, Kubernetes, and version control systems like Git or SVN for seamless software delivery.
  • Conduct system design reviews focusing on distributed systems, cloud infrastructure, scalability, and high availability.
  • Stay abreast of emerging AI trends and technologies such as NLP, machine learning frameworks (TensorFlow, PyTorch), and big data tools (NoSQL databases like MongoDB or Cassandra).
  • Define end-to-end technical architecture for AI/ML solutions: data flow, model integration, RAG/agentic patterns, APIs, and infrastructure
  • Select the appropriate AI/ML approach for each use case (LLM vs. traditional

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