Job Title: Jr. Machine Learning/AI Engineer
Location: Jersey City, NJ
Duration: 12 months +
Job Description
As a Machine Learning/AI Engineer, you will be responsible for designing, developing, and deploying machine learning models and AI solutions using Python. You will collaborate with cross-functional teams to understand business requirements, identify opportunities for leveraging machine learning and AI technologies, and implement solutions to address complex problems. You will work with large datasets, utilize statistical analysis and machine learning techniques to derive insights, and build scalable and robust algorithms.
Key Responsibilities
- Problem Understanding: Collaborate with stakeholders to understand business problems and identify opportunities for applying machine learning and AI techniques.
- Data Collection: and Preprocessing: Gather, clean, and preprocess large datasets from various sources to prepare them for analysis and model training.
- Feature Engineering: Engineer features from raw data to improve model performance and interpretability.
- Model Training and Evaluation: Train, validate, and fine-tune machine learning models using appropriate evaluation metrics and validation techniques.
- Deployment: Deploy machine learning models into production environments, ensuring scalability, reliability, and performance.
- Monitoring and Maintenance: Monitor model performance in production, conduct periodic model retraining, and address any issues that arise.
- Documentation: Document code, algorithms, and processes to facilitate knowledge sharing and maintainability.
- Research and Innovation: Stay updated with the latest advancements in machine learning and AI research and explore innovative solutions to improve existing systems.
Requirements
- Over 3 years of Proven experience in developing and deploying machine learning models and AI solutions using Python. Experience with deep learning frameworks is highly desirable.
- Strong understanding of statistical concepts, linear algebra, calculus, and probability theory.
- Excellent problem-solving skills and the ability to translate business requirements into technical solutions.
- Effective communication skills to collaborate with cross-functional teams, present findings, and explain complex concepts to non-technical users, customers and stakeholders.
- Meticulous attention to detail in data analysis, model development, and code implementation.
- Willingness to learn new technologies and adapt to changing project requirements and priorities.
