We’re looking for, hands-on AI Engineer Intern who can design, prototype, and experiment with Agentic AI frameworks in real-world, high-stakes environments. You’ll work with a team of quants and engineers to integrate agentic reasoning with traditional ML workflows for use in financial data analysis, simulation, and autonomous decision-making.

What You’ll Do

Prototype agentic workflows using exsisting frameworks
Build and test multi-agent systems for portfolio optimization, risk management, or market monitoring
Design and run experiments for retrieval-augmented generation (RAG), chain-of-thought planning, and autonomous trading assistants
Assist in the development of core ML models
Work closely with quant researchers to turn research ideas into scalable code

What We’re Looking For

Must-Haves:

Strong Python skills
Experience with Agentic AI frameworks
Solid grasp of ML fundamentals (supervised/unsupervised learning, model evaluation, overfitting, feature engineering)
Experience with at least one ML/DL framework (PyTorch, TensorFlow, Scikit-learn)
Familiarity with prompt engineering, RAG
Ability to read and adapt research papers into working code

Nice-to-Haves:

Knowledge of quantitative finance or trading systems
Exposure to RLHF or fine-tuning LLMs

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