Machine Learning Engineer

Machine Learning Engineer

Serbia/India/North Macedonia (Hybrid)

We’re looking for a skilled Machine Learning Engineer to help revolutionize the Insurtech space. In this role, you’ll develop advanced ML models and work closely with cross-functional teams to drive innovation. Your contributions will directly enhance our data-driven solutions. Be part of a team that’s shaping the future of insurance technology.

What You’ll Be Doing

  • Design and implement advanced machine learning algorithms tailored to our data and use cases.
  • Build, train, and optimize machine learning models for seamless integration into business processes.
  • Collaborate with product managers, developers, and data scientists to ensure solutions align with business objectives.
  • Stay current with Insurtech trends to keep our ML capabilities innovative and competitive.
  • Test and validate models for accuracy, performance, and scalability in partnership with the QA team.
  • Document algorithms, model performance, and workflows for transparency and team alignment.
  • Contribute to team knowledge through continuous learning and mentorship of junior members.

What We’re Looking For

  • Proven experience as a Machine Learning Engineer, Data Scientist, or in a similar role.
  • Strong proficiency in Python (or other languages) and familiarity with libraries (e.g., Scikit-learn,
    TensorFlow, PyTorch).
  • Expertise in machine learning, deep learning, and data mining in enterprise environments.
  • Hands-on experience with MLOps, including model development, deployment, and monitoring.
  • Experience with cloud platforms (AWS, Google Cloud, or Azure) and scalable model deployment.
  • Familiarity with NLP or computer vision techniques is highly desirable.
  • Understanding of data engineering, pipelines, and architecture.
  • Experience with Docker, Kubernetes, and containerized model deployment.
  • BA Degree in Computer Science, Data Science, Math, or related field
  • Knowledge of the insurance sector is beneficial but not required














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