onsite
Machine Learning Engineer - Compare The Market
ML Engineer
Lead end‑to‑end ML projects, building scalable models on AWS, to simplify financial decisions for millions. Focus on data pipelines, model training, and production deployment using Python and advanced ML techniques.
About the role
Key Responsibilities
- Design, develop, and deploy production‑grade machine learning models that power financial decision tools.
- Build and maintain robust data pipelines on AWS (S3, Glue, Redshift) to ingest, clean, and transform large datasets.
- Collaborate with data scientists and product teams to translate business requirements into scalable ML solutions.
- Implement model monitoring, A/B testing, and continuous improvement loops to ensure high performance and reliability.
- Document architecture, code, and best practices for internal knowledge sharing.
Requirements
- 3+ years of experience in machine learning engineering or data science roles.
- Strong proficiency in Python, including libraries such as Pandas, Scikit‑learn, TensorFlow or PyTorch.
- Hands‑on experience with AWS services (SageMaker, Lambda, EC2, S3, Glue).
- Solid understanding of data engineering concepts and experience building ETL pipelines.
- Excellent problem‑solving skills and ability to communicate complex ideas to cross‑functional teams.
Skills
pythonmachine learningawsdeep learning