AI Product & Research Engineer: Prototyping & MLOps

We are a leading trading platform that is ambitiously expanding to the four corners of the globe. Our top-rated products have won prestigious industry awards for their cutting-edge technology and seamless client experience. We deliver only the best, so we are always in search of the best people to join our ever-growing talented team.
We’re spinning up a brand-new AI department and need afirst-on-the-groundengineer who can turn wild LLM ideas into working demos overnight, teach everyone around you why automation matters, and have fun doing it.
If you can ship Python or Type Script prototypes, keep reading.

Responsibilities:

• Prompt Engineering & Orchestration:Craft, iterate, and optimize prompts for APIs (OpenAI, Cohere, Anthropic). Build multi-step “chains” using Lang Chain, Llama Index, or custom controllers. Develop and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling;
• Vector Search & Retrieval:Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features.
• Rapid Prototyping:Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, orNext.jsfor stakeholder feedback;
• MLOps & Deployment:Implement CI/CD pipelines (e.g., Git Lab Actions, Apache Airflow), experiment tracking (MLflow), and model monitoring for reliable production workflows;
• Cross-Functional Collaboration:Participate in code reviews, architectural discussions, and sprint planning to deliver features end-to-end.

Requirements:

• Master’s degree in AI and/or Computer Science;
• Hands-on experience integrating LLM APIs (e.g. OpenAI, Hugging Face Inference);
• Practical experience fine-tuning LLMs via OpenAI, Hugging Face or similar APIs;
• Strong proficiency in Python;
• Deep expertise in prompt engineering and tooling like Lang Chain or Llama Index;
• Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines;
• Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos.
• Familiarity with containerization (Docker) and at least one orchestration/deployment platform;
• Excellent communication and ability to frame AI solutions in business terms.

Nice-to-have:

• Familiarity with database systems (Postgre
  
  SQL, Mongo
  
  DB) and caching layers (Redis);
• Open-source contributions or published AI demos;
• Understanding of cost-optimization, monitoring for LLM usage and model-API trade-offs;
• Familiarity with prompt engineering best practices and “vibe-coding” tools (e.g., Git Hub Copilot, Cursor IDE);
• Willingness to travel occasionally for team offsites or workshops.