
Machine Learning Engineer
- Kuala Lumpur
- Permanent
- Full-time
- Able to frame and contextualize machine learning problems
- Research, build and design machine learning systems and models that can solve business problems
- Assess, understand and analyse data to select appropriate datasets for data modelling and testing
- Identify, prototype and implement appropriate machine learning algorithms and tools to improve our products
- Design, execute and monitor machine learning model experiments and metrics
- Perform statistical analysis to evaluate and prove business impact of modelling improvements
- Automate, architect and orchestrate machine learning processes and pipelines
- Monitor, optimise and maintain machine learning solutions in production
- Enrich existing machine learning libraries and model development frameworks to empower model development in MoneyLion
- Work with MLOps Engineers and Data Scientists to improve systems designs and architectures
- Enforce test-driven development and advocate engineering best practices to reduce technical debt in machine learning systems
- Develop tools and internal libraries to facilitate model governance over the machine learning development lifecycle
- Strong mathematical, statistical or actuarial background
- Good programming knowledge and software engineering skills
- Must have hands on experience in machine learning, predictive analytics and statistical modelling
- Experience developing and deploying machine learning models in production.
- Adept at problem solving and troubleshooting using both textbook methods and novel viewpoints
- Proficient in Python and SQL
- Proficient with machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM
- Preferably have experience in building machine learning systems and workflows
- Preferably familiar and have experience with MLOps tools such as DVC, MLFlow, Metaflow, Seldon, BentoML
- Preferably familiar and have experience with AWS, Docker, Kubernetes.
- Solid communication and collaboration skills.
- Take-Home Assessment
- Interview & Discussion of Take-Home Assessment - Hiring Manager (Virtual or face-to-face)