BMG Money is seeking an AI/ML Engineer to revolutionize fintech by building intelligent systems that power real-time decisions, risk assessments, fraud detection, and customer experiences, making financial services smarter, faster, and more accessible.
Requirements
- 3+ years of hands-on experience building, deploying, and maintaining production-grade ML models, ideally within fintech, banking, or other high-risk, regulated domains.
- A deep understanding of machine learning techniques, including classification, regression, clustering, and time series forecasting, coupled with the ability to select and apply the right models for complex financial problems.
- Strong Python skills and deep knowledge of key ML/DL libraries (e.g., Scikit-learn, PyTorch, TensorFlow, XGBoost).
- Experience with cloud platforms (e.g., GCP, AWS, Azure) and containerized environments (e.g., Docker, Kubernetes).
- Comfortable with Git, collaborative workflows, and agile development methodologies.
- Direct experience with fraud detection, credit scoring, or real-time decision-making systems.
- Familiarity with Large Language Models (LLMs) or Natural Language Processing (NLP) use cases in fintech (e.g., document parsing, sentiment analysis, chatbot optimization).
Responsibilities
- Design, train, and fine-tune machine learning and deep learning models that tackle critical fintech challenges—including credit scoring, risk modeling, and recommendation systems.
- Ensure accuracy, explainability, and robustness for all production-deployed models.
- Collect, clean, and process large, high-dimensional financial datasets (both structured and unstructured).
- Conduct exploratory data analysis (EDA) to identify hidden patterns, anomalies, and strategic opportunities for automation or insight.
- Collaborate closely with engineering teams to seamlessly deploy models into scalable microservices or APIs.
- Own the full ML lifecycle—from initial experimentation and validation to ongoing monitoring and retraining.
- Continuously research and integrate the latest advancements in AI, Large Language Models (LLMs), MLOps, and broader fintech AI trends.
Other
- Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.
- Direct experience working with financial data, KPIs (Key Performance Indicators), or compliance-sensitive systems is a significant advantage.
- Contributions to open-source projects or relevant publications in ML/AI.
- A strong understanding of data privacy, model explainability (XAI), and fairness principles as applied to financial AI applications.
- US Remote
