AI Data Engineer

Key Responsibilities:

1- Model Engineering: Implement and optimize LLM pipelines using both proprietary (OpenAI, Anthropic) and open-source models (Llama, Mistral) to find the best cost-to-performance ratio.

2- RAG Architecture: Build and maintain sophisticated RAG systems to ensure agents have real-time access to our internal documentation, customer history, and trading platform data.

3- Prompt Engineering & Tuning: Develop and version-control complex prompt templates, utilizing techniques like Few-Shot prompting and Chain-of-Thought to improve reasoning.

4- Evaluation & Testing: Establish “LLM-as-a-judge” frameworks and automated testing to measure hallucination rates, toxicity, and accuracy before agents hit the non-production environments.

5- Tool-Use Logic: Program the logic that allows agents to use external tools (APIs, Database queries, Moltbot functions) reliably and safely.

6- Latency Optimization: Streamline model inference and data retrieval to ensure the “Super Employee” responds in near real-time, improving customer satisfaction.

7- DevOps for AI: Familiarity with LLMOps tools for monitoring model performance and drift in production.

Additional qualifications:

• Experience in high-throughput environments (e.g., Trading, FinTech, or Global Logistics).
• -Previous experience building and deploying autonomous agents in a production environment.
• -Familiarity with Claude and OpenClaw ecosystem.