Senior Machine Learning Engineer
New
J
JobgetherPayments technology
Based in IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Senior-level experience
- Required Skills
- AWSPythonKerasPyTorchTensorflowA/B testingscikit-learnMLOps
Requirements
- Have senior-level experience in machine learning engineering, data science, or applied research.
- Have successfully taken models into production through APIs, batch processing, or streaming systems.
- Demonstrate strong Python programming skills and hands-on experience with machine learning libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch, or Keras.
- Be able to design and run experiments, conduct A/B testing, and interpret results.
- Have experience taking models from research through deployment, monitoring, and continuous improvement in production.
- Understand modern software engineering practices, infrastructure, data tooling, and the machine learning lifecycle.
- Understand machine learning platform operations, including deployment workflows, versioning, monitoring, and observability.
- Be familiar with reinforcement learning concepts, particularly multi-armed or contextual bandits; production experience is not required.
- Have experience with cloud infrastructure; AWS is preferred, while Google Cloud Platform or Microsoft Azure experience is also suitable.
- Be able to independently research, develop, test, and productionize solutions and make technical decisions in ambiguous situations.
- Collaborate with product, data, engineering, and business stakeholders, including engaging directly with customers.
- Exposure to payments or e-commerce is advantageous but not required.
- Be willing to help shape a developing machine learning function and establish practices without a fully defined roadmap.
Responsibilities
- Own machine learning features from research and experimentation through model training, deployment, monitoring, and optimization.
- Develop and productionize machine learning models for intelligent payment routing.
- Build and improve model versioning, CI/CD pipelines, observability, and production monitoring.
- Design and run experiments, including A/B tests, to evaluate model performance and measurable improvements.
- Partner with product, data, and engineering teams to identify use cases and ship measurable features.
- Speak directly with customers to validate assumptions, understand requirements, and assess solutions in practice.
- Help define priorities, evaluate technical trade-offs, and select machine learning opportunities.
- Mentor engineers adopting machine learning techniques and share implementation practices.
- Establish repeatable practices that improve the reliability, maintainability, and scalability of machine learning solutions.
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