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Senior Machine Learning Engineer
Location
India
Posted
103 days ago
Salary
0
Seniority
Senior
Job Description
Senior Machine Learning Engineer
ItsaCheckmate
• Model Development: Design and build next-generation ML models using advanced tools like PyTorch, Gemini, and Amazon SageMaker - primarily on Google Cloud or AWS platforms. • Feature Engineering: Build robust feature pipelines; extract, clean, and transform large-scale transactional and behavioral data. Engineer features like time-based attributes, aggregated order metrics, categorical encodings (LabelEncoder, frequency encoding). • Experimentation & Evaluation: Define metrics, run A/B tests, conduct cross-validation, and analyze model performance to guide iterative improvements. Train and tune regression models (XGBoost, LightGBM, scikit-learn, TensorFlow/Keras) to minimize MAE/RMSE and maximize R². • Own the entire modeling lifecycle end-to-end, including feature creation, model development, testing, experimentation, monitoring, explainability, and model maintenance. • Monitoring & Maintenance: Implement logging, monitoring, and alerting for model drift and data-quality issues; schedule retraining workflows. • Collaboration & Mentorship: Collaborate closely with data science, engineering, and product teams to define, explore, and implement solutions to open-ended problems that advance the capabilities and applications of Checkmate, mentor junior engineers on best practices in ML engineering. • Documentation & Communication: Produce clear documentation of model architecture, data schemas, and operational procedures; present findings to technical and non-technical stakeholders.
Job Requirements
- Academics: Bachelors/Master’s degree in Computer Science, Engineering, Statistics, or related field
- Experience: 5+ years of industry experience (or 1+ year post-PhD)
- Building and deploying advanced machine learning models that drive business impact
- Proven experience shipping production-grade ML models and optimization systems, including expertise in experimentation and evaluation techniques.
- Hands-on experience building and maintaining scalable backend systems and ML inference pipelines for real-time or batch prediction
- Programming & Tools:
- Proficient in Python and libraries such as pandas, NumPy, scikit-learn; familiarity with TensorFlow or PyTorch.
- Hands-on with at least one cloud ML platform (AWS SageMaker, Google Vertex AI, or Azure ML).
- Data Engineering:
- Hands-on experience with SQL and NoSQL databases; comfortable working with Spark or similar distributed frameworks.
- Strong foundation in statistics, probability, and ML algorithms like XGBoost/LightGBM; ability to interpret model outputs and optimize for business metrics.
- Experience with categorical encoding strategies and feature selection.
- Solid understanding of regression metrics (MAE, RMSE, R²) and hyperparameter tuning.
- Cloud & DevOps: Proven skills deploying ML solutions in AWS, GCP, or Azure; knowledge of Docker, Kubernetes, and CI/CD pipelines
- Collaboration: Excellent communication skills; ability to translate complex technical concepts into clear, actionable insights.
- Must be comfortable working in US hours at least till 5 pm EST.
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