MLOPS Engineer

Job Summary

This hybrid role is for a senior developer who designs and delivers production ready generative AI and machine learning solutions using Google Cloud and modern 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 integration tests and performance tests to ensure reliable behavior in real world conditions.
• Document solution architectures model assumptions and operational procedures in a clear and structured manner that supports ongoing maintenance and knowledge sharing.
• Mentor peers on generative AI and MLOps best practices by sharing patterns reviewing code and encouraging consistent engineering standards.
• Align daily development work with the organization mission by prioritizing solutions that deliver measurable business outcomes and positive societal impact.

Qualifications

• Possess professional experience of eight to twelve years in designing and delivering production grade software solutions with a strong focus on machine learning and artificial intelligence.
• Demonstrate hands on expertise in generative AI techniques such as large language models and advanced neural architectures applied to real business use cases.
• Show deep understanding of AI and machine learning concepts including supervised learning unsupervised learning model evaluation and responsible AI considerations.
• Exhibit strong proficiency in MLOps practices such as continuous integration continuous delivery automated retraining model versioning and experiment tracking.
• Display solid experience with Docker for building optimizing and managing containers in hybrid or cloud based infrastructures.
• Show practical experience with Google Cloud machine learning services including managed training prediction and data processing capabilities.
• Demonstrate proficiency with Git based workflows including branching strategies and collaborative development in distributed teams.
• Prefer prior exposure to hybrid work environments that require effective communication self management and collaboration across remote and office locations.
• Value clean code practices automated testing and secure development principles that ensure resilient and maintainable AI solutions.

Certifications Required

Google Professional Machine Learning Engineer or equivalent AI and ML certification is preferred.