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AI Engineer (LLM Workflows & LLMOps) - Muhimma Digital

AI Engineer (LLM Workflows & LLMOps) - Muhimma Digital

Qureos IncSaudi Arabia, Saudi Arabia
منذ أكثر من 30 يومًا
الوصف الوظيفي

Own the build-out and day-to-day optimisation of agentic LLM workflows that ingest large volumes of qualitative text (survey verbatims, social posts, transcripts) and deliver production-grade data and insights for dashboards. You will translate research requirements into modular chains / agents, optimize and maintain existing workflows, and monitor performance in line with emerging LLMOps best practices

Key Responsibilities :

LLM Integration & Prompt Engineering

  • Design, test, and maintain workflows against OpenAI, Google, or open-weights models via SDKs or REST.
  • Help build multi-agent graphs (e.g., LangGraph) that route tasks such as classification, summarisation, and extraction

NLP Pipeline Development

  • Fine-tune or embed open-source models (e.g., sentence-transformers) to tag sentiment and themes in Arabic / English text at scale
  • Optimise vector-DB retrieval (Pinecone / Chroma) for hybrid RAG scenarios used by research analysts.
  • Cross-Functional Enablement

  • Pair with data engineers to keep ETL / ELT pipelines LLM-ready; write minimal documentation and how-to notebooks for researchers.
  • Act as a bridge between the research and data teams to understand key requirements and translate them into scalable solutions
  • Required Skills & Experience :

  • 1–3 years hands-on Python (FastAPI, LangChain / LlamaIndex)
  • Familiarity with cloud GPU instances (AWS Bedrock / SageMaker, Azure OpenAI, or Runpod)
  • Basic DevOps : Docker, git,
  • Applied NLP : tokenisation, embeddings, text classification; exposure to Arabic language models is a plus.
  • Comfortable reading research papers / blogs and translating them into PoCs quickly.
  • Strong written & spoken English; Arabic proficiency preferred.
  • Nice-to-Have :

  • Experience with Streamlit or Dash for quick internal prototypes.
  • Prior work analysing survey verbatims or focus-group transcripts.

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