The AI Developer is responsible for designing, developing, and deploying artificial intelligence solutions to solve complex business problems. This role involves working with machine learning algorithms, deep learning frameworks, and large-scale datasets to build intelligent systems that drive innovation, automation, and efficiency. The AI Developer collaborates closely with data scientists, software engineers, and business stakeholders to translate real-world challenges into scalable AI applications.

AI/ML Model Development:

• Implement machine learning and deep learning models under the guidance of senior staff.
• Apply supervised and unsupervised learning techniques to solve practical problems.
• Perform model evaluation and help with model optimization (e.g., hyperparameter tuning)
• Data Preparation & Feature Engineering:
• Assist in data exploration, cleaning, preprocessing, and transformation for ML pipelines.
• Develop and validate features that improve model performance and generalization.
• Work with structured and unstructured data, including text and images.

Model Integration & Deployment :

• Package and deploy models into applications or services (APIs, batch jobs).
• Collaborate with engineering teams to ensure seamless integration of AI components.
• Assist in testing and monitoring deployed models for accuracy and stability.

Research & Prototyping:

• Explore and experiment with new algorithms or techniques relevant to the project.
• Stay updated on industry trends and contribute to proof-of-concept solutions.
• Document findings and share insights with the team.

Collaboration & Communication :

• Work closely with data scientists, ML engineers, software developers, and product teams.
• Translate technical concepts into understandable terms for cross-functional stakeholders.
• Participate in code reviews, sprint planning, and daily stand-ups.

Best Practices & Documentation :

• Write clean, maintainable, and efficient code following team conventions.
• Maintain documentation for datasets, models, and experiments.
• Follow internal MLOps processes for model versioning and reproducibility.