Hybrid Machine Learning Engineer

Posted last month

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About the role

  • Assist in the development and optimization of machine learning models.
  • Preprocess and analyze datasets to ensure data quality.
  • Collaborate with senior engineers and data scientists on model deployment.
  • Conduct experiments and run machine learning tests.
  • Stay updated with the latest advancements in machine learning.

Requirements

  • Strong proficiency in Python for data analysis, machine learning, and automation.
  • Solid understanding of supervised and unsupervised AI/machine learning methods (e.g., XGBoost, LightGBM, Random Forest, clustering, isolation forests, autoencoders, neural networks, transformer-based architectures).
  • Experience in payment fraud, AML, KYC, or broader risk modeling within fintech or financial institutions.
  • Experience developing and deploying ML models in production using frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
  • Hands-on experience with LLMs (e.g., OpenAI, LLaMA, Claude, Mistral), including use of prompt engineering, retrieval-augmented generation (RAG), and agentic AI to support internal automation and risk workflows.
  • Ability to work cross-functionally with engineering, product, compliance, and operations teams.
  • Proven track record of translating complex ML insights into business actions or policy decisions.

Benefits

  • Flexible work environment
  • Employee shares options
  • Health and life insurance
  • Wellness programs

Job title

Machine Learning Engineer

Job type

Experience level

JuniorMid level

Salary

Not specified

Degree requirement

Bachelor's Degree

Location requirements

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