Machine Learning Engineer
About the role
As a Machine Learning Engineer at an innovative technology company with a global presence, you will be responsible for building and deploying machine learning systems in production, owning projects end-to-end, and contributing to the transformation of complex challenges into actionable insights for businesses. The role involves working with Python, ML libraries, deep learning frameworks, and MLOps tooling, as well as designing and deploying ML systems, including model serving and monitoring. You will also work with cloud platforms and developer tools, and may mentor junior engineers or lead technical discussions. The company values a passion for solving real-world problems using AI and data, strong analytical and communication skills, and a collaborative mindset.
Must have
- Minimum 1-2 years of hands-on experience building and deploying machine learning systems in production, with a track record of owning projects end-to-end
- Deep proficiency in Python, with the ability to write clean, efficient, and robust code that others can build on — including experience setting or enforcing code quality standards (testing, code review, CI/CD)
- Strong command of core ML concepts and algorithms, with the judgment to choose the right approach for a given problem, data constraint, or latency/accuracy trade-off
- Extensive hands-on experience with ML libraries (e.g., scikit-learn, NumPy, pandas), deep learning frameworks (e.g., TensorFlow, PyTorch), and MLOps tooling (e.g., MLflow, Kubeflow, Airflow)
- Proven experience designing and deploying ML systems in production, including model serving, monitoring, versioning, and retraining pipelines
- Strong experience with cloud platforms (GCP, AWS, or Azure), including provisioning infrastructure for training and inference at scale
- Solid experience with developer tools including Git, Docker, and Kubernetes
- Bachelor’s degree in computer science, Engineering, or a related field
- Experience mentoring junior engineers or leading technical design discussions
Nice to have
- Experience working on real-world problems in the finance/AI domain is a strong advantage
- Familiarity with distributed training or large-scale data processing (e.g., Spark, Ray) is a plus
- Participation in Kaggle competitions, publications, or open-source ML contributions is a bonus
Technologies
- Airflow
- AWS
- Azure
- CI/CD
- Deep Learning
- Distributed Training
- Docker
- GCP
- Git
- Kubeflow
- Kubernetes
- Machine Learning
- MLflow
- MLOps
- NumPy
- pandas
- Python
- PyTorch
- scikit-learn
- Spark
- TensorFlow
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