Design AI systems, including data pipelines, model deployment, and integration with other systems to ensure seamless operation and data flow.
Ensure the scalability, reliability, and performance of AI systems.
Collaborate with cross-functional teams to integrate AI models into production environments.
Build, train, and optimize machine learning models by selecting appropriate algorithms, performing hyperparameter tuning, and conducting model optimization to achieve high accuracy and performance.
Evaluate and validate model performance using various metrics and techniques to ensure models meet the required standards and deliver actionable insights.
Work end-to-end on the ML lifecycle, from data exploration to model operationalization.
Collaborate with data engineers, data scientists, product, etc, in a multi-functional team to deliver and maintain the solutions and business integration.
Partner with different teams and domains on designing, explaining, and implementing ML models.
You will be responsible for supporting users with the solutions that you will build.
Work with the MLOps engineer in the team on operationalization of the models, which can be through batch inference, or live through providing API’s.
Integrate your models with existing systems or make them consumer-facing.
Ensure high-quality solutions are delivered through testing, applying engineering standards, and actively working on model monitoring.
Requirements & Skills:
Strong experience working with Artificial Intelligence and Machine Learning, and delivering business value through applying ML.
Advanced degree in computer science, math, statistics, engineering, or a related field.
Experience in building AI models/platforms, including Gen AI.
Experience with Python, ML libraries (such as scikit-learn, pytorch, etc), SQL, Spark, pandas, and cloud technologies.
Experience in designing and running live model tests.
Have strong knowledge of the whole model lifecycle from exploring data to bringing machine learning solutions to production and integration.
Experience with containerizing ML workloads, using Docker and Kubernetes
Background in software engineering, and experience with CI/CD, testing & creating microservices is highly preferred.
