AI / ML Lead

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.

Skills

MLOps, PyTorch, Python, SageMaker, Spark, TensorFlow, Vertex AI