Role & responsibilities
We are seeking a highly skilled and motivated AI/ML Engineer to design, develop, and deploy state-of-the-art machine learning models and AI-driven systems. The ideal candidate will have a strong background in machine learning frameworks, programming, and data analysis, coupled with the ability to work in a collaborative, fast-paced environment.
Model Development:
- Design, implement, and optimize machine learning models for various applications, including prediction, classification, clustering, and recommendation systems.
- Data Pipeline Management:
- Develop and maintain data pipelines, ensuring data is clean, reliable, and suitable for training and testing machine learning models.
Research and Innovation:
- Stay updated on the latest AI/ML trends, techniques, and tools to drive innovation within the team.
- Prototype and test new algorithms and methods to solve complex problems.
System Integration:
- Collaborate with software engineers to integrate AI/ML solutions into existing platforms and applications.
Performance Monitoring:
- Monitor and analyze the performance of deployed models, implementing improvements as needed to maintain high accuracy and efficiency.
Collaboration:
- Work closely with cross-functional teams, including data scientists, product managers, and business analysts, to align AI/ML solutions with business objectives.
Preferred candidate profile
- Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or a related field.
- Strong programming skills in Python, R, or similar languages.
- Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Proficiency in working with large datasets and databases (SQL, NoSQL, etc.).
- Solid understanding of algorithms, data structures, and mathematical concepts (linear algebra, probability, statistics).
- Experience with cloud platforms like AWS, Azure, or Google Cloud for AI/ML deployment.
- Strong problem-solving skills and attention to detail.
Preferred Skills
- Experience with natural language processing (NLP) or computer vision (CV) techniques.
- Familiarity with MLOps practices and tools.
- Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
- Published research or contributions to open-source AI/ML projects.
