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Arc Exclusive
Arc Exclusive

Ai/Data Engineer - Part-time - Worldwide

Location

Remote anywhere

Hourly rate

Hourly rate

Min. experience

5+ years

Hours per week

30 hours

Duration

4 weeks

Required skills

PythonTensorFlow

Freelance job
Posted 5 hours ago
Apply now
Actively recruiting / 20 applicants

We’re here to help you

Cynthia is in direct contact with the company and can answer any questions you may have. Email

CynthiaCynthia, Recruiter

About the Role

Our client, Handlet, is building an AI-powered platform that takes in customer messages and data, understands the intent, and automatically drafts and sends optimized responses. The platform aims to be self-learning and self-improving, using historical data, corrections, and user preferences to generate better responses over time.

We are looking for an experienced AI/Data Engineer to join the Handlet team and help take the platform to the next level. This is a remote, contract role with an initial 4-week timeline, with potential for extension.

Responsibilities

  • Integrate large language models (LLMs) and other AI/ML components into the Handlet platform
  • Develop a vector database to store and retrieve contextual data for the AI engine
  • Implement data engineering workflows to ingest, process, and feed data into the AI models
  • Optimize the AI models to generate increasingly accurate and personalized responses based on historical data and user feedback
  • Work closely with the existing development team to ensure seamless integration of the AI components

Required Skills

  • 2-3+ years of experience working with large language models and AI/ML systems
  • Strong knowledge of vector databases and data engineering principles
  • Proficiency in Python, TensorFlow/PyTorch, and other relevant AI/ML frameworks
  • Experience integrating AI/ML components into production software systems
  • Familiarity with natural language processing and generation techniques

Bonus (Nice to Have)

  • Experience with N8N or similar workflow automation tools
  • Understanding of RAG (Retrieval-Augmented Generation) systems

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