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Data Scientist - Support Top-Tier Entrepreneurs

RayAI Inc.

Remote · South Africa Full-time 1w ago

About the role

A Message from Our CEO

We are seeking a highly skilled Data Scientist with deep expertise in modern AI/ML systems, including LLMs, multimodal models, fine-tuning techniques, and advanced retrieval architectures. In this role, you will design, prototype, and deploy AI-powered solutions that leverage state-of-the-art language, vision, and agentic frameworks. You will work closely with engineering, product, and research teams across the US and Europe to bring cutting-edge AI capabilities into production environments.

Responsibilities

  • Design, build, and optimize LLM-powered systems using OpenAI, Anthropic, and open-source/local model families.
  • Architect and implement RAG pipelines, including hybrid search, query rewriting, prompt optimization, and reranking strategies.
  • Develop and maintain vector database infrastructures (Pinecone, Weaviate, Qdrant) for large-scale embedding storage and fast retrieval.
  • Train, evaluate, and retrain embedding models for domain-specific semantic search and knowledge retrieval.
  • Build and integrate multimodal AI solutions using OCR, CLIP, and modern vision architectures for text-image understanding.
  • Apply fine-tuning techniques (LoRA/QLoRA) to adapt foundation models to organizational datasets and specialized tasks.
  • Develop production‑ready AI applications using Python, PyTorch, and modern orchestration frameworks.
  • Implement LLM orchestration with LangChain or LlamaIndex, including evaluators, tool abstractions, memory, and RAG components.
  • Establish robust evaluation frameworks to measure model performance, reduce hallucination, and ensure reliability in production.
  • Build agentic workflows using AutoGen, CrewAI, or similar frameworks to power automation and multi‑agent collaboration systems.
  • Stay current with research trends and apply theoretical and practical insights in Generative AI to drive innovation across the organization.

Requirements

  • Experience in applied machine learning or data science, with at least 2 years focused specifically on LLMs or Generative AI.
  • Demonstrated experience building end‑to‑end RAG, fine‑tuning, or multimodal AI systems.
  • Strong proficiency in Python, PyTorch, and AI tooling ecosystems.
  • Experience deploying models at scale in production environments.
  • Strong understanding of evaluation metrics, model reliability, and safety/reduction of hallucination.
  • Familiarity with vector embeddings, vector databases, and semantic search.
  • Experience with agent frameworks such as AutoGen, CrewAI, or LangGraph‑like toolkits.
  • Experience with distributed training, model optimization, quantization, or GPU acceleration.
  • Knowledge of DevOps/MLOps tooling for deploying LLM‑based systems.
  • Contributions to open‑source LLM or RAG projects.

What We Offer

  • Competitive salary and performance‑based bonuses.
  • Fully remote, flexible work environment.
  • Modern laptop and hardware provided by us.
  • Specialized training in AI, automation, and digital productivity tools.
  • Global exposure—collaborate with top‑tier founders and fast‑growing startups.
  • Continuous learning and career growth opportunities in an international environment.

Requirements

  • Experience in applied machine learning or data science, with at least 2 years focused specifically on LLMs or Generative AI.
  • Demonstrated experience building end‑to‑end RAG, fine‑tuning, or multimodal AI systems.
  • Strong proficiency in Python, PyTorch, and AI tooling ecosystems.
  • Experience deploying models at scale in production environments.
  • Strong understanding of evaluation metrics, model reliability, and safety/reduction of hallucination.
  • Familiarity with vector embeddings, vector databases, and semantic search.
  • Experience with agent frameworks such as AutoGen, CrewAI, or LangGraph‑like toolkits.
  • Experience with distributed training, model optimization, quantization, or GPU acceleration.
  • Knowledge of DevOps/MLOps tooling for deploying LLM‑based systems.
  • Contributions to open‑source LLM or RAG projects.

Responsibilities

  • Design, build, and optimize LLM-powered systems using OpenAI, Anthropic, and open-source/local model families.
  • Architect and implement RAG pipelines, including hybrid search, query rewriting, prompt optimization, and reranking strategies.
  • Develop and maintain vector database infrastructures (Pinecone, Weaviate, Qdrant) for large-scale embedding storage and fast retrieval.
  • Train, evaluate, and retrain embedding models for domain-specific semantic search and knowledge retrieval.
  • Build and integrate multimodal AI solutions using OCR, CLIP, and modern vision architectures for text-image understanding.
  • Apply fine-tuning techniques (LoRA/QLoRA) to adapt foundation models to organizational datasets and specialized tasks.
  • Develop production‑ready AI applications using Python, PyTorch, and modern orchestration frameworks.
  • Implement LLM orchestration with LangChain or LlamaIndex, including evaluators, tool abstractions, memory, and RAG components.
  • Establish robust evaluation frameworks to measure model performance, reduce hallucination, and ensure reliability in production.
  • Build agentic workflows using AutoGen, CrewAI, or similar frameworks to power automation and multi‑agent collaboration systems.
  • Stay current with research trends and apply theoretical and practical insights in Generative AI to drive innovation across the organization.

Benefits

performance-based bonusesModern laptop and hardware providedSpecialized training in AI, automation, and digital productivity toolsContinuous learning and career growth opportunities

Skills

AutoGenCLIPCrewAIGenerative AILangChainLangGraphLLMLlamaIndexLoRAMLOpsOCROpenAIPineconePythonPyTorchQLoRARAGWeaviate

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