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Huzzle is a global talent marketplace and recruitment service that identifies and places professionals directly into its clients' teams rather than acting as the direct employer for these positions.

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H
Posted 3mo ago

Remote AI Engineer

Huzzle
Croatia
RemoteFull Time
Responsibilities
  • Design solutions
  • Deploy models
  • Collaborate teams
Requirements
  • 3+ years in AI/ML or software engineering
  • Strong Python and ML frameworks
  • Experience with LLMs and AI platforms
  • Cloud deployment
  • API/backend architecture
  • Docker/Git/CI/CD
  • Remote collaboration
  • Bonus: AI agents/automation
Technical tools mentioned
PythonTensorFlowPyTorchLangChainHuggingFaceOpenAI APIAnthropicGeminiDockerGitCI/CDRAGembeddingsAWSGCPAzure

Job description

About Huzzle

At Huzzle, we connect exceptional talents with top opportunities at leading companies across the UK, US, Canada, Europe, and Australia. Our clients include startups, digital agencies, and tech platforms in industries such as SaaS, MarTech, FinTech, and AI.

Unlike an outsourcing agency, we place you directly with a client where you’re hired in-house as a valued member of their team.

Role Type: Full-time

Engagement: Independent Contractor

Job Summary

As an AI Engineer, you will be responsible for developing and optimizing AI systems, integrating machine learning models into production environments, and building scalable AI-driven applications. You will contribute to everything from model experimentation to deployment and performance optimization.

Key Responsibilities

  • Design, build, and deploy AI and machine learning solutions for real-world business applications.
  • Develop and maintain applications using Large Language Models (LLMs) such as GPT-based systems.
  • Build AI-powered automations, copilots, chatbots, and workflow systems.
  • Fine-tune, evaluate, and optimize machine learning models for performance and scalability.
  • Integrate AI systems with APIs, databases, CRMs, and third-party platforms.
  • Collaborate with product and engineering teams to translate business requirements into AI-driven solutions.
  • Implement prompt engineering strategies and retrieval-augmented generation (RAG) systems.
  • Monitor model performance, reliability, and cost efficiency in production environments.
  • Maintain documentation, testing procedures, and deployment workflows.
  • Stay updated on the latest AI trends, frameworks, and emerging technologies.

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