
AI/ML Engineer
1 day ago
*Key Responsibilities:*
- AI/ML Solution Development: Design, build, and optimize machine learning models and algorithms for various applications, including natural language processing, computer vision, recommendation systems, and predictive analytics.
- Data Collection and Preprocessing: Collaborate with data engineers to identify, acquire, and preprocess datasets, ensuring high quality and suitability for model training.
- Model Training and Evaluation: Implement state-of-the-art AI/ML algorithms using frameworks like TensorFlow, PyTorch, or scikit-learn, and conduct rigorous testing and evaluation to measure performance and accuracy.
- Deployment and Integration: Deploy AI models into production environments and integrate them into existing systems for efficient and reliable execution.
- Algorithm Optimization: Continuously optimize and improve existing AI/ML models to enhance performance and scalability.
- Research and Innovation: Stay current with the latest advancements in AI/ML research and technology, applying innovative approaches to address business challenges.
- Collaborative Teamwork: Work closely with cross-functional teams to understand business requirements, develop technical solutions, and communicate project progress effectively.
- Documentation: Maintain clear and concise documentation of AI/ML models, algorithms, and codebases to facilitate understanding and future enhancements.
- Data Privacy and Security: Ensure compliance with data privacy and security protocols, protecting sensitive information.
Requirements:
- Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph.D. in a relevant domain is a plus.
- Technical Skills: Proficiency in programming languages such as Python, R, or Java, with experience in popular AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, Keras, scikit-learn).
- Machine Learning Expertise: Strong understanding of various machine learning algorithms, deep learning architectures, and statistical methods. Experience with supervised and unsupervised learning techniques.
- Data Handling: Knowledge of data preprocessing, feature engineering, and data visualization techniques.
- Software Development: Familiarity with software development principles, version control, and agile methodologies.
- Problem-Solving Skills: Ability to analyze complex problems, develop innovative solutions, and troubleshoot issues effectively.
- Communication: Excellent verbal and written communication skills, capable of conveying technical concepts to both technical and non-technical stakeholders.
- Team Player: Collaborative mindset with the ability to work well within a team environment.
Job Type:
- Full-time
Schedule:
- Day shift
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