This hybrid role is for a senior developer who designs and delivers production ready generative AI and machine learning solutions using Google Cloud and up-to-date MLOps practices. The role involves building scalable models containerizing services with Docker and ensuring high quality deployments that align with enterprise standards while creating measurable value for customers and society.
Responsibilities
- Design and implement scalable generative AI applications that address complex business problems while ensuring robust performance and reliability across hybrid deployment environments.
- Develop and optimize machine learning pipelines that automate data preparation model training evaluation and deployment using industry standard MLOps practices.
- Build containerized services using Docker so that AI and machine learning components can be deployed consistently across development testing and production environments.
- Create reusable components for data ingestion feature engineering and model serving that improve development efficiency and maintainability within the team.
- Integrate Google Cloud machine learning services into end to end solutions that leverage managed platforms for training prediction monitoring and governance.
- Implement version control best practices using Git by managing branches code reviews and collaborative workflows that maintain code quality and traceability.
- Apply core artificial intelligence and machine learning concepts to select suitable algorithms tune hyperparameters and validate models against clearly defined success metrics.
- Collaborate with product and domain teams to translate requirements into technical designs that balance feasibility performance and long term sustainability.
- Establish monitoring logging and alerting for models in production so that performance drift data quality issues and operational risks are detected early.
- Conduct rigorous testing of models and pipelines including unit tests integratio