Mid-Level ML Engineer – Data Science and ML Ops – AI Led Bank


At Tintra, our reason for being, or Ikigai, is simple. We enable fully borderless financial inclusivity, measured through behaviour, rather than by background.

With the experience and reputation of over 16 years working with some of the world’s leading corporations. We are experts at solutions for cross-border deals and international trade transactions. We pride ourselves on our local relationships around the world to help good people and their businesses operating from complicated places.

We believe in a world without inherent bias or prejudice. We want everyone to be given a fair opportunity. That is why our patentable artificial intelligence is free from human bias, while providing best-in-class risk mitigation.

“We are building emerging market focused banking technology and infrastructure to enable financial institutions, multinationals and large corporates in the emerging world to access global banking infrastructure without bias or prejudice in an environment where we control the compliance exposure from end to end”, CEO, Tintra plc.

Job Summary:

We are seeking a highly skilled and experienced Mid-Level ML Engineer with expertise ranging from data science to ML ops, and a strong background in bringing ML models to production. The successful candidate will be responsible for developing and implementing scalable, high-performance machine learning systems and deploying them to production environments. This role involves working collaboratively with a team of data scientists, ML engineers, and software developers, and contributing to the development of ML strategies and best practices. The role will be agile and changeable as the business grows and adapts over the coming months, with a mix of development and support in strategizing for the principal product strategies.

Key Responsibilities:

  • Train and optimize deep learning models using techniques such as transfer learning, data augmentation, and hyperparameter tuning.
  • Develop, test, and deploy deep machine learning models and algorithms in production environments.
  • Collaborate with data scientists, ML engineers, and software developers to implement scalable, high-performance machine learning systems.
  • Develop and maintain ML pipelines for data processing, feature engineering, and model training.
  • Deploy deep learning models to production environments using ML ops tools and techniques, such as containerization, continuous integration and deployment (CI/CD), and monitoring and logging.
  • Implement and maintain monitoring and alerting systems for deployed machine learning models.
  • Develop and maintain ML infrastructure and tools to support model development, testing, and deployment.
  • Contribute to the development of ML strategies and best practices.
  • Develop and maintain documentation and best practices for deep learning model development and deployment.
  • Stay up-to-date with the latest research and tools in deep learning, and apply them to improve our ML solutions.


  • Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field.
  • Minimum of 3-5 years of experience in machine learning engineering or data science.
  • Strong programming skills, including proficiency in Python and experience with libraries such as NumPy, Pandas, and Scikit-learn.
  • Experience with ML infrastructure and tools, such as MLflow, Kubeflow, or SageMaker.
  • Experience with containerization technologies such as Docker and Kubernetes.
  • Knowledge of software engineering best practices, such as version control, automated testing, and continuous integration and deployment.
  • Experience with cloud computing platforms such as AWS, Azure, or GCP.
  • Strong communication skills, including the ability to communicate technical concepts to non-technical stakeholders.
  • Ability to work collaboratively with a team of data scientists, ML engineers, and software developers.