Machine Learning Engineer

Summary

Join Tiger Analytics, a global AI and analytics consulting firm, as a highly skilled MLE/MLOps Engineer. You will play a critical role in building and maintaining robust machine learning infrastructure, ensuring seamless integration between ML models and production systems. This position requires designing, implementing, and maintaining scalable MLOps pipelines. You will collaborate with data scientists and software engineers to productionize ML models and develop CI/CD workflows. Experience with real-time data processing and managing infrastructure using tools like Docker, Kubernetes, and Terraform is essential. The ideal candidate will monitor system performance and implement best practices in software engineering. This is an excellent opportunity for career development in a fast-growing environment.

Requirements

• Bachelor's or Master's degree in Computer Science, Software Engineering, or related field
• 7+ years of experience in software engineering / ML engineering with a strong programming foundation (Python, Java, or Scala)
• Proven experience with MLOps tools and frameworks for model deployment and lifecycle management
• Hands-on experience with Apache Spark Streaming and real-time data processing
• Solid understanding of cloud platforms (preferably Azure)
• Experience with version control (Git), containerization (Docker), and orchestration (Kubernetes)
• Familiarity with CI/CD tools like Jenkins, GitHub Actions, or Azure DevOps

Responsibilities

• Design, implement, and maintain scalable and reliable MLOps pipelines for model training, deployment, and monitoring
• Collaborate with data scientists and software engineers to productionize ML models
• Develop and maintain CI/CD workflows for ML systems and model lifecycle management
• Work with real-time data using Apache Spark Streaming to support high-throughput data processing pipelines
• Ensure high availability and performance of ML services in production
• Manage and automate infrastructure using tools such as Docker, Kubernetes, and Terraform
• Monitor and improve system performance, model drift, and data quality issues
• Implement best practices in software engineering including code reviews, testing, and documentation

Preferred Qualifications

• Experience with Azure ML or other managed ML platforms (e.g., SageMaker, Vertex AI)
• Exposure to ML model performance monitoring and alerting tools
• Knowledge of ML model testing, data validation, and reproducibility
• Experience working in an Agile development environment