Machine Learning Engineer

Job summary

The role involves working as a Machine Learning Engineer within the MLOps team, focusing on deploying, maintaining, and monitoring machine learning models in production environments. The position requires collaboration with data scientists and software engineers to enhance AI-driven solutions.

Qualifications

• Proven experience in deploying machine learning models in production on cloud platforms (GCP, AWS, Azure).
• Familiarity with CI/CD pipelines for machine learning (e.g., Github actions, Docker).
• Experience with ML platforms/frameworks (e.g., VertexAI, Kubeflow, Sagemaker).
• Knowledge of data processing frameworks (e.g., Spark, Databricks), especially Apache Beam/Dataflow is preferred.
• Understanding of model monitoring and maintenance in production.
• Experience in performance and cost optimization (e.g., Latency, Throughput).
• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong problem-solving and troubleshooting skills.
• Effective communication skills for cross-team collaboration.

Responsibilities

• Collaborate with data scientists to deploy machine learning models, ensuring high performance and scalability.
• Develop and maintain data and model pipelines for efficient workflows.
• Design and implement CI/CD pipelines for machine learning model deployment.
• Monitor the performance of deployed models to ensure reliability and quality.
• Work with cross-functional teams to create solutions that meet business needs while following best practices.
• Continuously improve infrastructure for AI model production and delivery.
• Develop MCP servers and A2A agents for managing multi-agent orchestrated deployments.

Skills

• Machine learning model deployment
• CI/CD for ML
• Data processing frameworks
• Performance optimization
• Python programming

Education

• Relevant degree in Computer Science, Engineering, or a related field is typically expected.

Tools

• Cloud platforms (GCP, AWS, Azure)
• CI/CD tools (e.g., Github actions, Docker)
• ML frameworks (e.g., TensorFlow, PyTorch)
• Data processing tools (e.g., Spark, Databricks)