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Artificial Intelligence Specialist
3 weeks ago
We are seeking an experienced AI specialist to develop, optimize, and deploy sophisticated Machine Learning and Deep Learning models.
Key Responsibilities:- Data Analysis & Feature Engineering: Conduct comprehensive feature engineering, data analysis, and data exploration to extract valuable insights from diverse datasets.
- Model Development & Optimization: Develop and optimize cutting-edge Machine Learning models to achieve high accuracy, efficiency, and performance.
- Deep Learning Implementation: Design and implement advanced Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques.
- Imbalanced Data Handling: Effectively handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness, generalization, and robustness.
- Model Deployment & Monitoring: Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on real-world feedback and performance metrics.
- Cross-Functional Collaboration: Collaborate seamlessly with cross-functional teams, including data scientists, software engineers, and product managers, to align ML solutions with overarching business goals.
- Statistical & Mathematical Application: Utilize fundamental statistical knowledge and strong mathematical principles to ensure the reliability, validity, and interpretability of models.
- Innovation & Research: Stay abreast of the latest advancements in ML and AI, actively bringing in new techniques and research findings to drive innovation within the team and company.
- Hands-on Experience: Hands-on experience in Machine Learning and Deep Learning.
- Strong Expertise: Strong expertise in feature engineering, data exploration, and data preprocessing.
- Experience with Imbalanced Datasets: Experience with imbalanced datasets and techniques to improve model generalization.
- Proficiency in Programming Languages: Proficiency in Python, TensorFlow, Scikit-learn, and other relevant ML frameworks and libraries.
- Strong Mathematical and Statistical Knowledge: Strong mathematical and statistical knowledge coupled with excellent problem-solving skills.
- Ability to Optimize Models: Ability to optimize models for high accuracy and performance in real-world scenarios.
- Big Data Technologies: Experience with Big Data technologies (e.g., Hadoop, Spark).
- Containerization and Orchestration Tools: Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
- MLOps Practices: Experience in automating ML pipelines with MLOps practices.
- Cloud Platforms: Experience in model deployment using cloud platforms (e.g., AWS, GCP, Azure) or MLOps tools.
