For our business partner - a dynamic company with challenging projects using cutting-edge technologies, we are looking for Machine Learning Engineers to be part of a team that will work on integrating financial and industry data, research, and news into tools that will generate intelligence for their beneficiaries to base their business and financial decisions on.
The tools created will help track performance, generate alpha, identify investment ideas, understand competitive and industry dynamics, perform valuation, and assess credit risk.
As an ML Engineer, you will work on multiple data science projects in collaboration with internal and external project owners on the product, commercial, and data team. You will be responsible for providing machine learning engineers support, create a data pipeline for modeling, scale models, develop APIs to help move machine learning models in productions. You will collaborate with data scientists and production-oriented software engineers.
- Construct machine learning lifecycle management including data collection, normalization, and standardization within a data pipeline construction.
- Develop AutoML infrastructure for model selection and hyperparameter tuning.
- Create applications and interface to present the output of ML models.
- Experiment, develop, and produce high-quality machine learning services and platforms to make huge technology and business impact.
- Develop a hosting platform for machine learning models.
- Create pipelines to query and retrieve and update data for existing applications to keep them updated.
- Supervise the scaling and management of the machine learning modeling ecosystem.
- Work alongside data scientists and product owners to improve aspects of their lines of business through machine learning.
- At least 3 years experience with big-data technologies such as Hadoop, Spark, SparkML, etc.
- Advanced proficiency in multiple machine learning programming languages including Python, PySpark, or Scala.
- Expertise in application, data, and infrastructure architecture disciplines
- Advanced knowledge of architecture and design across all systems
- Able to understand various data structures and common methods in data transformation.
- Familiarity with MLOps and ModelOps
- Knowledge of Docker, Kubernetes, AWS is an advantage.
- In-depth knowledge of mathematics, statistics, and algorithms
- Great communication and collaboration skills.
- Excellent time management and organizational abilities.
- Fluent in English (written and verbal).
- Bachelor’s in computer science, Mathematics, Statistics, Artificial Intelligence, Data Science, Machine Learning, or related fields required.
- Private medical insurance;
- Access to training for self-development;
- Lunch tickets;
- Flexible benefits basket;
- Fun room and various team bonding and team building programs;
- Attractive compensation scheme;
- Progressive vacation days.