AI/ML Engineer

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.

1. 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.
2. System Integration:
• Collaborate with software engineers to integrate AI/ML solutions into existing platforms and applications.
3. Performance Monitoring:
• Monitor and analyze the performance of deployed models, implementing improvements as needed to maintain high accuracy and efficiency.
4. 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.
• 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.