About This Role
This senior Machine Learning Engineer role focuses on designing, implementing, and maintaining the full machine learning lifecycle, from data preparation to deployment and retraining. The position involves ensuring the continuous improvement of AI solutions through MLOps practices and collaborating with Data Scientists and Data Engineers to build scalable AI solutions.
Responsibilities
- Design, implement, and maintain the full machine learning lifecycle, including data preparation, feature engineering, model training, validation, deployment, and retraining
- Ensure the operation, monitoring, and continuous improvement of research-ready and production-ready AI solutions through MLOps practices
- Collaborate closely with Data Scientists and Data Engineers to define technical requirements and build scalable AI solutions
- Develop and operationalize proof-of-concept and proof-of-value initiatives using state-of-the-art AI and machine learning frameworks
- Support the deployment and maintenance of AI models in production environments, ensuring reliability, scalability, and performance
- Promote knowledge sharing and contribute to the adoption of emerging AI technologies across the organization
- Drive continuous improvements in machine learning engineering processes, tooling, and delivery practices
Requirements
- 3-5 years of proven experience in Machine Learning Engineering or related AI engineering roles
- Strong programming skills in Python or similar languages used for Machine Learning development
- Experience with Machine Learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Knowledge of data preparation, feature engineering, and model optimization techniques
- Experience designing and implementing end-to-end machine learning solutions for research and production environments
- Hands-on experience with model deployment, APIs, and MLOps practices
- Familiarity with cloud-based AI platforms such as Azure ML, Snowflake, and dbt
- Working knowledge of SQL and modern data pipelines
- Experience training and refining AI models with business applications and measurable impact
- Understanding of DevOps and MLOps principles for scalable AI solution delivery
- Experience working collaboratively with Data Science and Data Engineering teams
- English (minimum B2)
Nice to have:
- Experience implementing proof-of-concept and proof-of-value AI initiatives
- Knowledge of Generative AI, Large Language Models (LLMs), and emerging AI technologies
- Experience with cloud-native AI architectures and distributed computing environments
- Strong critical thinking and attention to detail
