Overview
As a Junior Machine Learning Engineer, you will be responsible for developing, optimizing, and deploying machine learning models for real-world applications. You will work closely with data scientists and software engineers to enhance AI-driven solutions, analyze data, and contribute to the continuous improvement of ML models. This role is ideal for candidates with at least one year of experience in AI/ML, looking to advance their expertise in the field.
Responsibilities
- Design, develop, and optimize machine learning models for various business applications.
- Collect, clean, and preprocess data for model training and evaluation.
- Fine-tune and optimize ML algorithms for better performance and efficiency.
- Implement and monitor deployed models to ensure continuous improvements.
- Collaborate with cross-functional teams to integrate AI/ML solutions into production environments.
- Develop automated pipelines for data preprocessing, model training, and evaluation.
- Stay up to date with the latest advancements in AI/ML technologies and frameworks.
- Document research, experiments, and model performance for knowledge sharing.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- 1+ years of hands-on experience in machine learning model development and deployment.
- Strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, or Scikit-learn.
- Experience in data preprocessing, feature engineering, and handling large datasets.
- Understanding of deep learning architectures, including CNNs, RNNs, and Transformers.
- Familiarity with cloud platforms (AWS, GCP, or Azure) for model deployment and scalability.
- Experience with databases (SQL, NoSQL) and data storage solutions.
- Proficiency in version control systems such as Git.
- Strong analytical and problem-solving skills with a keen eye for detail.
- Ability to work independently and in a collaborative team environment.
Skills
- Strong programming skills in Python with expertise in ML frameworks.
- Experience with supervised and unsupervised learning techniques.
- Knowledge of neural networks and deep learning architectures.
- Familiarity with NLP and computer vision applications.
- Experience in model evaluation using metrics like precision, recall, and F1-score.
- Proficiency in handling real-world datasets and feature engineering.
- Experience with containerization tools like Docker is a plus.
- Good understanding of MLOps concepts and automated ML pipelines.
- Strong communication and documentation skills for sharing research and results.
