We are seeking an experienced and highly skilled AI Architect to join our growing technology team.

This critical role will be responsible for designing, building, and deploying our next generation of AI solutions.

While our current operations leverage pre-trained models, the successful candidate will be instrumental in leading the transition to a capability that includes custom model setup, training, and optimization, significantly strengthening our internal AI development expertise.

Key Responsibilities :

AI Strategy and Architecture :

- Define the technical architecture, development standards, and best practices for building scalable, high-performance AI/ML systems.

- Evaluate and select the appropriate technology stack, tools, and platforms for model development, training, deployment, and monitoring (e.g., cloud platforms, MLOps tools, data storage solutions).

- Lead the transition from relying solely on pre-trained models to establishing robust internal capabilities for custom model development and training.

- Ensure that AI solutions are compliant with data governance, security, and privacy regulations.

Model Development and Training :

- Design and implement end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, validation, and testing.

- Set up and manage the necessary infrastructure (e.g., GPU clusters, distributed computing frameworks) required for efficient, large-scale model training.

- Develop, train, and fine-tune various types of machine learning models (e.g., deep learning, natural language processing, computer vision) tailored to specific business problems.

- Conduct performance tuning, hyperparameter optimization, and iterative experimentation to achieve state-of-the-art results.

MLOps and Deployment :

- Establish and maintain robust MLOps practices for seamless integration, continuous training (CT), continuous delivery (CD), and model monitoring in production environments.

- Develop scalable APIs and services to integrate trained models into core business applications.

- Monitor deployed models for drift, performance degradation, and data quality issues, implementing effective retraining and update strategies.

Leadership and Mentorship :

- Provide technical guidance and mentorship to junior data scientists and engineers on model development best practices, training techniques, and architectural decisions.

- Collaborate closely with product managers, data engineers, and domain experts to translate business requirements into technical AI specifications.