Machine Learning Developer

Job highlights

• Qualification- Bachelor s or master s degree in computer science,Machine Learning,Data Science,or a related field (or equivalent work experience)
• Proven experience as a Machine Learning Developer or Data Scientist,with a portfolio of machine learning projects
• Experience with deep learning frameworks (e.g.,TensorFlow,PyTorch) for neural network development is a plus

Qualification- Bachelor’s or master’s degree in computer science, Machine Learning, Data Science, or a related field (or equivalent work experience).

Experience- 2 to 4 years

Skills-

Excellent communication and teamwork skills.

Proven experience as a Machine Learning Developer or Data Scientist, with a portfolio of machine learning projects.

Strong programming skills in Python and proficiency in machine learning libraries and frameworks.

Knowledge of data preprocessing, feature engineering, and model evaluation techniques.

Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) for neural network development is a plus.

Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization (e.g., Docker).

Strong problem-solving and analytical skills.

Salary- As per industry standards

Responsibilities

Model Development: Design, develop, and implement machine learning models and algorithms to address business challenges.

Data Preparation: Gather, preprocess, and clean large datasets, ensuring data quality and suitability for machine learning tasks.

Feature Engineering: Create relevant features and data transformations to enhance model performance.

Model Training: Train and evaluate machine learning models using appropriate frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).

Hyperparameter Tuning: Optimize model hyperparameters and architectures to improve model accuracy and efficiency.

Validation and Testing: Conduct cross-validation, performance testing, and A/B testing to assess model performance and robustness.

Deployment: Deploy machine learning models into production environments, integrating them into business applications or workflows.

Monitoring and Maintenance: Implement monitoring and maintenance procedures to ensure model accuracy and reliability in production.

Documentation: Document model development processes, code, and results for future reference and collaboration.

Collaboration: Collaborate with data scientists, software developers, and domain experts to understand business needs and deliver effective ML solutions.

Stay Updated: Keep up to date with the latest developments in machine learning, AI, and related fields.