Distributed Cloud | AI/Machine Learning Engineer
Indexed description
Job Description
We are seeking a versatile and passionate AI / Machine Learning Engineer to join our data science and engineering team. You will be instrumental in bridging the gap between data science research and production-ready applications, building scalable machine learning systems that drive business value. This role requires a strong balance of software engineering principles and ML expertise.
Key Responsibilities
- Design, develop, and implement end-to-end Machine Learning pipelines for training, testing, and deployment of predictive models.
- Work closely with Data Scientists to translate prototypes and models into scalable, production-grade code (MLOps).
- Develop robust, efficient, and well-documented code primarily using Python and relevant ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Implement monitoring and alerting solutions for models in production to track performance, detect drift, and ensure reliability.
- Collaborate with Data Engineers to ensure efficient data preparation, feature engineering, and access to necessary data infrastructure.
- Stay current with the latest advancements in AI, ML techniques, and scalable infrastructure.
- 3+ years of professional experience in a Machine Learning Engineer, AI Developer, or similar role.
- Strong expertise in Python and object-oriented programming, with a focus on code quality and best practices.
- Mandatory practical experience with major Machine Learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn).
- Solid understanding of MLOps principles and the tools required for model deployment, versioning, and lifecycle management (e.g., MLflow, Kubeflow, or similar).
- Proficiency in SQL and experience working with large datasets and data warehousing concepts.
- Excellent analytical and problem-solving skills, with the ability to communicate complex technical concepts effectively.
- Experience working with Cloud platforms (AWS, GCP, or Azure) for model deployment, compute, and storage (e.g., SageMaker, Vertex AI, Azure ML Services).
- Knowledge of containerization technologies (Docker, Kubernetes) for building reproducible and scalable ML environments.
- Familiarity with distributed computing frameworks (e.g., Spark).
- Advanced degree (M.S. or Ph.D.) in Computer Science, Engineering, or a related quantitative field.
- Very good level of English, both spoken and written, for effective communication with international teams;
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