#18051-AI/ML Engineer
Key Responsibilities
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Design, develop, and deploy ML models and AI solutions across various domains such as NLP, computer vision, recommendation systems, time-series forecasting, etc.
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Perform data preprocessing, feature engineering, and model training using frameworks like TensorFlow, PyTorch, Scikit-learn, or similar.
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Collaborate with cross-functional teams to understand business problems and translate them into AI/ML solutions.
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Optimize models for performance, scalability, and reliability in production environments.
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Integrate ML pipelines with production systems using tools like MLflow, Airflow, Docker, or Kubernetes.
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Conduct rigorous model evaluation using metrics and validation techniques.
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Stay up-to-date with state-of-the-art AI/ML research and apply findings to enhance existing systems.
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Mentor junior engineers and contribute to best practices in ML engineering.
Required Skills & Qualifications
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Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
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4–8 years of hands-on experience in machine learning, deep learning, or applied AI.
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Proficiency in Python and ML libraries/frameworks (e.g., Scikit-learn, TensorFlow, PyTorch, XGBoost).
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Experience with data wrangling tools (Pandas, NumPy) and SQL/NoSQL databases.
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Familiarity with cloud platforms (AWS, GCP, or Azure) and ML tools (SageMaker, Vertex AI, etc.).
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Solid understanding of model deployment, monitoring, and CI/CD pipelines.
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Strong problem-solving skills and the ability to communicate technical concepts clearly.