Key Responsibilities:

• AI Development: Design, implement, and optimize machine learning models and AI-driven software solutions.

• Software Engineering: Develop high-quality, scalable, and maintainable code for AI applications.

• Data Processing: Work with large datasets, including data preprocessing, cleaning, and transformation for AI model training.

• Algorithm Optimization: Improve AI models for efficiency, accuracy, and performance.

• Integration: Deploy AI models into production systems and integrate them with existing software architecture.

• Research & Innovation: Stay up to date with advancements in AI, ML, and deep learning to implement cutting-edge techniques.

• Collaboration: Work closely with cross-functional teams, including data scientists, backend engineers, and product managers, to align AI solutions with business goals.

• Testing & Debugging: Ensure AI models and software components are thoroughly tested, debugged, and optimized for real-world deployment.

Qualification

Education:

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

Experience: 

• 5+ experience in software development and AI/ML model implementation.

• Experience with AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn.

Skills:

• Proficiency in Python, Java, or C++.

• Strong understanding of algorithms, data structures, and software architecture.

• Experience with cloud platforms (AWS, Azure, GCP) for AI model deployment.

• Familiarity with MLOps, CI/CD pipelines, and containerization (Docker, Kubernetes) is a plus.

• Problem-Solving: Strong analytical and problem-solving skills with the ability to tackle complex AI challenges.

• Communication: Excellent written and verbal communication skills, with the ability to present technical concepts to non-technical stakeholders.

• Preferred Qualifications:

• Experience with NLP, computer vision, or reinforcement learning.

• Knowledge of big data technologies (Hadoop, Spark) and databases (SQL, NoSQL).

• Exposure to AI ethics, bias mitigation, and responsible AI development practices.