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.

Experience
8-12 yrs