Senior Machine Learning Engineer
About the role
The Senior Machine Learning Engineer will join a global technology company focused on leveraging artificial intelligence to solve complex business challenges, particularly in the financial domain. The role involves leading the design, development, and deployment of machine learning models on large-scale datasets, setting technical direction, and mentoring junior engineers. Responsibilities include hands-on model building, establishing best practices for data and model workflows, and collaborating with cross-functional teams to deliver scalable ML solutions. Candidates should have a strong background in machine learning, experience with production ML systems, and a passion for impactful AI applications.
Must have
- 3+ 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
- Data Science
- Deep Learning
- Distributed Training
- Docker
- Feature Engineering
- Fintech
- GCP
- Git
- Kubeflow
- Kubernetes
- Machine Learning
- MLflow
- MLOps
- Model Evaluation
- NumPy
- pandas
- Python
- PyTorch
- scikit-learn
- Spark
- TensorFlow
Highlights
The company operates globally and offers opportunities to work on projects beyond the financial sector. There is an emphasis on professional growth, creativity, and cross-domain impact. The listing does not specify location, remote work, or application deadline.
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