Artificial Intelligence Engineer

Artificial Intelligence (AI) Engineer:

• Essential: Relevant Degree + 2-3yrs AI/ML, Quant Research or Data Science + FS/Inv
• 70,000 - 100,000 + 60% Bonus + Package
• Location: Abu Dhabi, UAE (relocation & visa supported)

Pear Talent is representing an international hedge fund, who are expanding theirteam in the UAE that build AI-driven tools used directly by portfolio managers and investment teams. As an AI Engineer you'll be a key member of a group of software engineers, quants, and data scientists that partner with investment professionals to build and scale bespoke front-office technology solutions across asset classes. Their remit spans data analytics, investment management and augmentation platforms, and cross-asset tooling with production-quality reliability.

Key accountabilities:

This is a hands-on AI engineering opportunity focused on developing analytics, ML models, and scalable data pipelines that support investment decision-making, risk analysis, and portfolio monitoring. The role spans Python-based modeling, Spark/Databricks pipelines, and building production-grade tools such as dashboards, APIs, and ML workflows that enable investment teams to generate insights and run scenarios independently.

This team operates at the intersection of engineering and investing partnering closely with PMs, quants, and data engineering to design and deploy practical AI solutions rather than purely experimental research.

Required experience:

• Degree in a technical/quantitative discipline (e.g., Data Science, Computer Science, Mathematics, Statistics, Financial Engineering, or related field)
• 2-3 years or more of experience in quantitative research, data science, or AI/ML-focused team ideally within financial markets or investment analytics.
• Experience in at least one asset class, with a practicalunderstanding of how data and models drive investment decisions.
• Strong proficiency in Python for analytics and modeling (Pandas, NumPy, scikit-learn, matplotlib, plotly, Jupyter); ability to prototype, test, and deploy end-to-end ML workflows.
• Good understanding of machine learning theory and applied techniques
• Hands-on familiarity with modern AI technologies and research
• Working knowledge of the data ecosystem (Spark, Databricks, or equivalent) focused on leveraging these for ML pipeline orchestration rather than pure ETL.
• Comfort with relational databases and SQL
• Strong communication skills able to explain AI concepts clearly to non-technical stakeholders and collaborate effectively across investment, data, and technology teams.
• Curious, collaborative, and experimental mindset, eager to explore new AI capabilities and translate them into tangible business and investment value.

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