Machine Learning Engineer

Job summary

The role involves working as a Machine Learning Engineer within an MLOps team, focusing on deploying and maintaining 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 cloud environments (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), with a preference for Apache Beam/Dataflow.
• 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.
• Excellent communication skills for effective team collaboration.

Responsibilities

• Collaborate with data scientists to transition machine learning models from development to production, ensuring 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 align with business needs and best practices.
• Continuously enhance infrastructure for AI model production and delivery.
• Develop MCP servers and A2A agents for orchestrated deployments.

Skills

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

Education

• Relevant technical background in machine learning and software engineering is preferred.