About the role
Responsibilities:
- Architect end-to-end machine learning systems, from data ingestion to model deployment and monitoring.
- Design and review scalable, high-performance ML architectures for large-scale production environments.
- Define and implement best practices for the ML lifecycle, including training, validation, deployment, versioning, and monitoring.
- Lead architecture decisions for model scaling, performance optimisation, and reliability.
- Collaborate closely with data engineers and platform teams to ensure seamless integration.
- Establish standards for reusability, modularity, and maintainability of AI/ML components.
- Guide the adoption of feature stores, model registries, and data lakes.
- Mentor senior data scientists and ML engineers on solution design, architecture patterns, and production readiness.
- Review technical designs, conduct architecture walkthroughs, and provide technical governance.
The core requirements for the job include the following:
Core AI/ML Skills:
- Strong proficiency in Python.
- Hands-on experience with TensorFlow and/or PyTorch.
- Deep understanding of deep learning architectures and model optimisation.
- Experience with distributed data processing using Spark.
MLOps and Platform:
- Strong knowledge of MLOps practices (CI/CD for ML, model versioning, monitoring, retraining).
- Experience with model registries, feature stores, and experiment tracking tools.
- Exposure to data lakes and large-scale data platforms.
Cloud and Tools:
- Hands-on experience with cloud AI platforms such as AWS Bedrock and SageMaker, Azure Machine Learning, and Google Cloud AI / Vertex AI.
- Strong understanding of cloud-native architectures and scalable ML deployments.