Machine Learning Systems Engineer

1 week ago


kolkata, India beBeeMachineLearning Full time

Job OpportunityWe are seeking a highly skilled Machine Learning Engineer to join our team. Your primary responsibility will be to design and develop large-scale, reliable machine learning systems.Develop and maintain AI/ML features for an open-source Observability Platform built on Grafana and ClickHouse.Collaborate with software engineers, service owners, and observability experts to ensure scalable, reliable ML model deployment.Design and manage data pipelines using Databricks and related tools.Use continuous integration and delivery (CI/CD) and MLOps best practices to automate model deployment and testing.Deploy and manage machine learning infrastructure on AWS or Azure.Set up and integrate MCP servers and connect tools across observability systems.Establish performance standards and develop custom MCP integrations between systems.Troubleshoot machine learning system performance and reliability using OpenTelemetry pipelines and observability metrics.Key Responsibilities:- Develop and maintain high-quality machine learning models.- Collaborate with cross-functional teams to deploy and manage machine learning infrastructure.- Design and implement data pipelines to support machine learning workflows.- Use CI/CD and MLOps best practices to automate model deployment and testing.Requirements:5+ years of experience in developing and deploying machine learning models.Proven track record of designing and implementing large-scale machine learning systems.Strong understanding of machine learning algorithms and statistical concepts.Experience with data pipeline management and ETL processes.Proficient in programming languages such as Python, Java, or C++.Familiarity with cloud-based platforms such as AWS or Azure.Bonus Points:- Experience with open-source technologies such as Grafana and ClickHouse.- Knowledge of continuous integration and delivery (CI/CD) tools such as Jenkins or GitLab CI/CD.- Familiarity with containerization using Docker.- Experience with monitoring and logging tools such as Prometheus or ELK Stack.



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