AI/ML Engineer @ Fundedfirm

Job Description

• *Design and deploy ML models* to score trader performance, risk exposure, and funding eligibility in real-time.
• Build AI systems to:

* Auto-journal trades and classify PnL events

* Detect trader manipulation, bots, or copy trading behavior

* Identify high-potential traders early (funding prioritization)

• Use LLMs and NLP to create smart summaries, trade notes, and insights from user behavior and support queries.
• Own the *AI infrastructure*, from experimentation to production (Docker, APIs, CI/CD).
• Collaborate closely with our backend, frontend, and R&D teams to bring AI features into the trader dashboards and CRM.
• Constantly explore new AI tools, algorithms, and research relevant to fintech and trading ecosystems.

*Ideal Candidate Profile*

• *4+ years of hands-on experience* in building and deploying ML/AI models in production environments.
• Solid foundation in *machine learning, deep learning, and statistics*.
• Expertise with tools like *Python, PyTorch/TensorFlow, Scikit-learn, XGBoost, HuggingFace*, etc.
• Strong data pipeline and API experience (Airflow, FastAPI/Flask, Pandas, SQL, MongoDB).
• Comfortable with deploying ML models at scale using *Docker, MLflow, AWS/GCP*.
• Bonus: Experience in *fintech, trading platforms, fraud detection, or real-time data systems*.
• Bonus++: Knowledge of reinforcement learning, anomaly detection, or building recommender systems.

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What Youll Build (Example Projects)

• *Traderscore AI*: A real-time rating engine to predict trader potential and risk based on behavior patterns.
• *AnomalyBot*: A system that flags suspicious trades and prevents fraud before payout.
• *Smart Journal*: NLP-powered auto trade logs with emotional sentiment tracking and decision reviews.
• *TopTraderRadar*: Predictive analytics dashboard to identify breakout talent globally.

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Keyskills:   Cloud platforms API development NLP & LLMs Data engineering tools Python Modeling frameworks NLP real-time systems Data manipulation & analysis trader analytics fintech