Machine Learning Engineer

Algorithm Development:
Design and implement advanced machine learning algorithms tailored for our datasets.

Model Creation:
Build, train, and refine machine learning models for business integration.

Collaboration:
Partner with product managers, developers, and data scientists to align machine learning solutions with business goals.

Industry Innovation:
Stay updated with Insurtech trends and ensure our solutions remain at the forefront.

Validation:
Test algorithms for accuracy and efficiency, collaborating with the QA team.

Documentation:
Maintain clear records of algorithms and models for team reference.

Professional Growth:
Engage in continuous learning and mentor junior team members.

• Proven experience as a Machine Learning Engineer, Data Scientist, or similar role
• Expertise in applying machine learning algorithms, deep learning, and data mining techniques in an enterprise environment
• Strong proficiency in Python (or other languages) and familiarity with libraries such as Scikit-learn, TensorFlow, PyTorch, or similar.
• Experience working with natural language processing (NLP) or computer vision is highly desirable.
• Understanding and experience with (MLOps), including model development, deployment, monitoring, and maintenance.
• Experience with cloud platforms (like AWS, Google Cloud, or Azure) and knowledge of deploying machine learning models at scale.
• Familiarity with data architecture, data engineering, and data pipeline tools.
• Familiarity with containerization technologies such as Docker, and orchestration systems like Kubernetes.
  Knowledge of the insurance sector is beneficial but not required.
• Bachelor s/Master s degree in Computer Science, Data Science, Mathematics, or a related field.