Job Description:
A Machine Learning Engineer designs, develops, and deploys scalable machine learning models that solve real-world problems. They work with large datasets, build data pipelines, and create algorithms that learn from data to deliver intelligent solutions across domains like finance, healthcare, and logistics. Their focus is not only on model accuracy but also on performance, reliability, and integration within production systems.
They bridge the gap between data science and software engineering, ensuring ML solutions are production-ready and optimized for efficiency. Working closely with data scientists, software developers, and product teams, ML engineers are responsible for automating decision-making processes through robust, efficient, and maintainable machine learning systems.
Responsibilities:
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Design and implement machine learning models for production.
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Build and maintain scalable data pipelines.
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Optimize models for performance and reliability.
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Collaborate with cross-functional teams on deployment.
Preferred Qualifications:
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Bachelor’s/Master’s in CS or related
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Strong Python and ML libraries (e.g. scikit-learn, XGBoost)
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Experience with TensorFlow or PyTorch
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Deep understanding of ML algorithms
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Proficient in data preprocessing
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Hands-on with model deployment
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Familiarity with cloud services (AWS/GCP)
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Experience with Docker and APIs
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Strong math and stats background
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Knowledge of MLOps tools
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Version control with Git
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Data pipeline frameworks (Airflow, etc.)
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Performance tuning and testing
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Excellent debugging and problem-solving skills
