Senior AI Engineer

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Requirements:

• Strong proficiency in Data Engineering, AI, and data science techniques, with advanced programming skills in Python.
• Deep understanding of machine learning algorithms, AI model development, and experience with AI frameworks such as TensorFlow.
• Hands-on experience with platforms including GitHub, AWS, Azure, and Snowflake.
• Knowledge of ML architecture tools and techniques for deploying models with accuracy and consistency.
• Experience in MLOps and DevOps, ensuring smooth integration and operationalization of AI models.
• Set standards and principles around code quality within the team and build exemplar code for others to follow.

Responsibilities:

• Leading the productionisation and integration of AI models, ensuring scalability, security, and maintainability across multiple projects.
• Identifying solutions for complex AI challenges and optimising infrastructure for performance and reliability.
• Overseeing AI model lifecycle management from deployment to retirement.
• Collaborating with key internal stakeholders, including IT, Operations, and Strategy teams, and influence senior management and external partners.
• Overseeing the technical engineering leadership of AI projects and ensure adherence to industry standards, regulation and ethical considerations.
• Overseeing and managing a technical engineering team with a focus on fostering collaboration, supporting professional growth, and enhancing team capabilities.
• Lead the management & maintenance of complex workflows with CI/CD, ensuring robust orchestration and continuous monitoring of AI systems' health and performance.
• Lead the deployment, management, and optimisation of ML models. Integrate AI with advanced DevOps practices and design scalable MLOps frameworks and container technologies.
• Provide guidance on dimensional modelling and lead the approach for data ingestion pipelines.
• Proficiency in using MLFlow for managing the AI /ML lifecycle, including experimentation, reproducibility, and deployment. Ability to integrate MLFlow with other tools and platforms.