Senior Machine Learning Engineer
New
J
JobgetherEnterprise order management
Based in IndiaContractSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLKubernetesPyTorchTypeScriptTensorflowscikit-learnMLOps
Requirements
- Demonstrated expertise in applied machine learning and data science, including taking models into production and measuring real-world performance.
- Strong Python skills and proficiency with machine learning tools and libraries such as PyTorch or TensorFlow, scikit-learn, pandas, and NumPy.
- Strong statistical foundations, including experimental design and appropriate evaluation metrics.
- Strong SQL skills and experience with large relational datasets.
- Experience building reliable scheduled data and feature pipelines that handle complex or messy source systems.
- Experience preparing machine learning solutions for handoff to operations or platform teams, including packaging, documentation, and runtime requirements.
- Comfort working directly with customers and delivery teams where requirements and data conditions vary.
- TypeScript or JavaScript proficiency sufficient to integrate machine learning capabilities with a broader product stack.
- Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related technical field, or equivalent practical experience.
- Working knowledge of Kubernetes and containerized deployment is preferred.
- Experience with MLOps tools such as MLflow, Kubeflow, Weights & Biases, Airflow, or Dagster is preferred.
- Experience with LLMs and agentic workflows, production-scale recommender systems, time series forecasting, enterprise ERP data, or cloud platforms is advantageous.
Responsibilities
- Design, train, evaluate, and improve models for prediction, ranking, recommendation, churn and propensity scoring, demand forecasting, and related order management use cases.
- Contribute across problem framing, data preparation, feature engineering, training, evaluation, and retraining strategy.
- Partner with product management to define measurable machine learning problems from roadmap priorities.
- Profile enterprise data, tune and validate models, and support customer delivery with implementation and solution engineering teams.
- Build reproducible Python training pipelines and experiment tracking.
- Profile, clean, and validate enterprise SAP and relational data, and identify data limitations affecting modeling.
- Package models and pipelines for production deployment and define monitoring for drift, performance regression, and data quality.
- Work with IT and platform teams to diagnose production issues and integrate model outputs through documented service interfaces.
- Apply responsible AI practices, including bias evaluation and explainability, and document model behavior, assumptions, limitations, and trade-offs.
- Review peers' work and contribute to standards for modeling rigor, reproducibility, and engineering quality.
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