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Qualification:
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
- Published papers or contributions to AI/ML research.
- Experience with reinforcement learning or generative models.
- Familiarity with explainability and interpretability frameworks for machine learning models (e.g., SHAP, LIME).
- Previous experience working with large-scale datasets or in a cloud-based environment (AWS, GCP, Azure).
Experience:
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- 3+ years of experience in machine learning, AI, or data science roles, with hands-on experience developing and deploying models in production environments.
- Strong experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar tools.
- Solid understanding of data structures, algorithms, and computer science fundamentals.
- Experience with deep learning techniques (CNNs, RNNs, GANs, etc.) and natural language processing (NLP) is highly desirable.
- Experience with cloud platforms (AWS, Azure, GCP) for deploying AI/ML models is a plus.
- Familiarity with model deployment tools such as Docker, Kubernetes, or MLOps platforms is a plus.
Skill :
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- Proficiency in programming languages like Python, R, or Java, with a focus on data science and AI libraries (e.g., NumPy, pandas, Matplotlib).
- Hands-on experience with databases (SQL, NoSQL) and big data technologies (e.g., Hadoop, Spark).
- Understanding of version control systems like Git for collaborative development.
- Familiarity with cloud infrastructure (AWS SageMaker, Google AI Platform, etc.) and containerization technologies (Docker, Kubernetes) is a plus.
- Strong understanding of AI ethics and responsible AI practices.
- Design, implement, and train machine learning and deep learning models to solve real-world problems, including supervised and unsupervised learning, reinforcement learning, and natural language processing (NLP).
- Develop algorithms and techniques for predictive analytics, recommendation systems, classification, regression, and anomaly detection.
- Use frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn for model development and experimentation.
- Clean, preprocess, and analyze large datasets to extract meaningful features for machine learning models.
- Implement data augmentation, feature extraction, and transformation techniques to improve model accuracy.
- Collaborate with data engineers to ensure that the data pipeline supports efficient feature engineering and model training.
- Evaluate the performance of machine learning models using standard metrics (e.g., accuracy, precision, recall, AUC, F1 score) and validate models with real-world data.
- Optimize models for speed, scalability, and accuracy, including hyperparameter tuning, cross-validation, and using advanced techniques like ensemble learning or neural architecture search.
- Continuously improve and fine-tune models based on feedback and new data.
- Monitor and maintain the performance of deployed machine learning models in production.
- Develop tools and processes for model versioning, model retraining, and monitoring model drift.
- Address model degradation and ensure that models are continuously improved as new data becomes available.
- Document machine learning workflows, model architectures, and codebases to ensure reproducibility and transparency.
- Create training materials and provide knowledge-sharing sessions to empower other teams to leverage AI/ML capabilities.
- Ability to collaborate effectively with cross-functional teams, including developers, IT operations, and business stakeholders.
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