Job Description:
As a Machine Learning Engineer at our digital bank, you will be instrumental in bridging the gap between machine learning development and operations. Your role is crucial in ensuring the seamless integration, deployment, and management of machine learning models with our backend microservices to deliver the AI capabilities. Working closely with data engineers, data scientists, backend engineers and cross-functional teams, you will contribute to the optimization and reliability of our AI-driven digital banking platform.
Technology stack we use:
AWS, Python, Redshift, Airflow, Kafka, Spark, EKS, Metaflow, MLflow, FeastFS, Flink
Key Responsibilities:
- Model Productionalization : Spearhead the design and implementation of MLOps practices, ensuring a smooth transition from machine learning development to production deployment.
- Infrastructure Orchestration: Utilize containerization tools like Docker and container orchestration platforms such as Kubernetes to optimize the deployment and management of machine learning models.
- Pipeline Development: Lead the design and development of scalable, modular, and maintainable MLOps pipelines, ensuring the efficient flow of data and models across the development, testing, and production environments.
- Monitoring and Optimization: Establish robust monitoring systems to track model performance in real-time, actively identifying areas for optimization to enhance scalability, efficiency, and reliability.
- Feature Store: management of a robust feature store, ensuring the efficient storage, retrieval, and versioning of machine learning features.
- CI/CD and Automation: Implement and enhance CI/CD pipelines for machine learning models, utilizing platforms such as Github Actions to streamline the development-to-production workflow.
- Model Versioning and Governance: Establish and maintain a robust system for versioning machine learning models, ensuring proper governance and documentation of model changes over time.
- Continuous Benchmarking: Continuously benchmark MLOps workflows against industry best practices, identifying opportunities for improvement and innovation to stay ahead in the rapidly evolving field.
- Knowledge Sharing: Organize and conduct training sessions for data scientists to ensure adherence to best practices and proficiency in utilizing the latest MLOps tools.
Requirements:
- Bachelor's degree in Computer Science, Engineering, or a related field.
- At least 5 years of experience in machine learning role or relevant experience in data science or engineering.
- Proficient in programming language (Python, Java) and scripting language. Hands-on experience with MLOps frameworks and libraries. (Metaflow, MLflow etc)
- Strong problem-solving and troubleshooting skills, with a focus on delivering reliable and scalable machine learning solutions.
- Excellent communication and interpersonal skills, capable of effectively collaborating with diverse stakeholders such as data scientists, data engineers, backend developers and product owners.
- Familiarity with Docker and Container Orchestration (Kubernetes)
- Familiarity with CI/CD platforms (Jenkins, Bitbucket, Github Actions, Gitlab CI)
- Familiarity with modern cloud service provider such as AWS, Azure, and GCP
- Agility and a proactive attitude towards embracing new technologies, contributing to the continuous innovation of our machine learning capabilities.
