Job Overview:
We are seeking a skilled and motivated Machine Learning Engineer (Mid-Level) to join our dynamic team. The ideal candidate will have hands-on experience in designing, building, and deploying machine learning models and algorithms. You will collaborate with cross-functional teams to create data-driven solutions that drive business growth and innovation.
Key Responsibilities
- Design, develop, and deploy machine learning models and algorithms using Python and associated libraries.
- Collaborate with data scientists, engineers, and product teams to understand business problems and translate them into ML solutions.
- Build and maintain scalable data pipelines for model training and deployment.
- Optimize and fine-tune ML models for performance and accuracy.
- Evaluate and implement appropriate ML frameworks, tools, and techniques.
- Deploy and monitor models in production environments, ensuring reliability and efficiency.
- Stay updated on the latest advancements in machine learning and AI technologies.
- Document processes, experiments, and results for knowledge sharing and reproducibility.
Qualifications and Skills:
- Education: Bachelor’s degree in Computer Science a related field.
- Experience: 2-4 years of hands-on experience in machine learning and Python development.
- Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
- Strong understanding of machine learning algorithms (e.g., regression, classification, clustering, neural networks).
- Experience with data preprocessing, feature engineering, and model evaluation techniques.
- Familiarity with cloud platforms like AWS, Azure, or GCP for ML model deployment.
- Knowledge of version control systems like Git.
- Strong problem-solving and analytical skills.
- Excellent communication and teamwork abilities.
Preferred Skills:
- Experience with deep learning techniques and frameworks.
- Knowledge of Natural Language Processing (NLP) or Computer Vision (CV).
- Familiarity with containerization tools like Docker and orchestration systems like Kubernetes.
- Hands-on experience with ML Ops tools and practices.
- Knowledge of SQL and data visualization tools (e.g., Tableau, Power BI) is a plus.