AI Product & Research Engineer

دبي - الإمارات

الراتب شهرياً: لم يكشف

تاريخ النشر: 09-11-2025

عدد الوظائف الشاغرة: 1 عدد الوظائف الشاغرة

سجل للتقديم

ملخص الوظيفة

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.

Were 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 TypeScript prototypes keep reading.

Responsibilities:

Prompt Engineering & Orchestration:Craft iterate and optimize prompts for APIs (OpenAI Cohere Anthropic). Build multi-step chains using LangChain LlamaIndex 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 stakeholder feedback;
MLOps & Deployment:Implement CI/CD pipelines (e.g. GitLab 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:

Masters 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 HuggingFace or similar APIs;
Strong proficiency in Python;
Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex;
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 (PostgreSQL MongoDB) 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. GitHub Copilot Cursor IDE);
Willingness to travel occasionally for team offsites or workshops.

What you will get in return:

Competitive Salary: We believe great work deserves great pay! Your skills and talents will be rewarded with a salary that makes you feel valued and motivated.

Work-Life Harmony: Join a company that genuinely cares about you -because your life outside of work matters just as much as your time on the clock.

Annual Performance Bonus: Your hard work doesnt go unnoticed! Celebrate your achievements with a well-deserved annual bonus tied to your performance.

Generous Time Off: Need a breather Our annual leave policy lets you recharge and enjoy life outside of work without a worry.

Employee Referral Program: Love working here Share the love! Bring your talented friends on board and get rewarded for growing our awesome team.

Comprehensive Health Benefits: Weve got your back with a medical insurance plan. Plus location-specific benefits and perks!

Workation Wonderland: Live your digital nomad dreams with 30 extra days to work remotely from anywhere in the world (some restrictions apply). Adventure awaits!

Volunteer Days: Make a difference! Take two additional paid days each year to support causes you care about and give back to the community.

Be a key player at the forefront of the digital assets movement propelling your career to new heights! Join a dynamic and rapidly expanding company that values and rewards talent initiative and creativity. Work alongside one of the most brilliant teams in the industry.

We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.

If you can ship Python or TypeScript prototypes keep reading.

Responsibilities:

Prompt Engineering & Orchestration:Craft iterate and optimize prompts for APIs (OpenAI Cohere Anthropic). Build multi-step chains using LangChain LlamaIndex 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 stakeholder feedback;
MLOps & Deployment:Implement CI/CD pipelines (e.g. GitLab 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:

Masters 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 HuggingFace or similar APIs;
Strong proficiency in Python;
Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex;
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 (PostgreSQL MongoDB) 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. GitHub Copilot Cursor IDE);
Willingness to travel occasionally for team offsites or workshops.