Job Description

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. 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.