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:

1. 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
2. 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.
3. 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.