The Senior Artificial Intelligence/Machine Learning Engineer is a key member of the MLOps team within a cross-functional development environment. This role involves close collaboration with data scientists, software engineers, and other stakeholders to operationalize machine learning models by deploying, maintaining, and monitoring them efficiently in production. The engineer contributes to enhancing model performance and infrastructure, supporting AI-driven solutions that address complex business challenges.
Responsibilities:
- Model Deployment: Collaborate with data scientists to transition machine learning models from development to production, ensuring high performance and scalability.
- Build Pipelines: Develop and maintain data and model pipelines that integrate seamlessly with existing systems to support reliable and efficient workflows.
- CI/CD for ML: Design and implement continuous integration and delivery pipelines to streamline the deployment process of machine learning models.
- Model Monitoring: Monitor the performance of deployed machine learning models to ensure reliability, scalability, and quality over time.
- Collaboration: Work with cross-functional teams to design solutions that meet business needs while adhering to best practices in machine learning and software engineering.
- Optimization: Continuously improve infrastructure to maintain cutting-edge AI model production and delivery capabilities.
Technologies and Tools:
- Cloud platforms: Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure
- CI/CD tools: GitHub Actions, Docker
- ML platforms/frameworks: VertexAI (Enterprise Agent Platform), Kubeflow, SageMaker
- Data processing frameworks: Apache Beam, Dataflow (highly desirable)
- Programming languages and libraries: Python, TensorFlow, PyTorch, scikit-learn
Key Skills and Competencies:
- Strong foundation in both machine learning and software engineering
- Experience deploying machine learning models in production environments
- Expertise in building and maintaining CI/CD pipelines for ML
- Knowledge of monitoring and maintaining models post-deployment
- Problem-solving and troubleshooting skills related to models and pipelines
- Performance and cost optimization experience (e.g., latency, throughput) is highly desirable
- Excellent communication skills for effective cross-team collaboration
Technologies: Cloud platforms (GCP, AWS, Azure), VertexAI, Kubeflow, SageMaker, Python, TensorFlow, PyTorch, scikit-learn, Apache Beam, Dataflow, Docker, CI/CD, GitHub Actions.
Last Updated: September, 2026
