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Machine Learning Engineer
IronOnelinkedin.com
Oct 20, 2026, 10:18 AM1w ago
Full-timeColombo, Western ProvinceML & AIMid level (1+ yr)
About the role
The Machine Learning Engineer will join a global technology company focused on leveraging artificial intelligence to solve complex business challenges. The role involves building and deploying machine learning systems in production, owning projects end-to-end, and collaborating with cross-functional teams. Key responsibilities include designing and maintaining ML pipelines, ensuring code quality, working with cloud platforms, and mentoring junior engineers. The company values innovation, technical excellence, and a collaborative approach to problem-solving.
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
- 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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