Machine Learning Engineer

The candidate will be required to be on my client payroll. Must Have Skills- MLOps Fundamentals: CI/CD/CT Pipelines of ML with Azure Demo, Python, SQL Specific contribution expected from the role: For a Machine Learning Engineer (MLE) with 4 years of experience, you are looking for a "Senior" level candidate. At this stage, the focus shifts from just "building models" to MLOps, scalability, and system integration. They need to bridge the gap between a Data Scientist s research and a Software Engineer s production environment. Production Deployment: Lead the transition of ML models from experimental notebooks to scalable, high-availability production services (API-based or batch). MLOps & CI/CD: Build and maintain automated ML pipelines for model training, testing, and deployment using tools like Kubeflow, MLflow, or SageMaker. Architectural Design: Design distributed systems and microservices that handle high-throughput inference requests with low latency. Feature Engineering & Data: Collaborate with Data Engineers to build robust Feature Stores and optimized ETL processes using PySpark or Flink. Performance Monitoring: Implement advanced monitoring for model drift, data skew, and system health to ensure long-term model reliability. Optimization: Optimize model inference performance (quantization, pruning, or hardware acceleration) for cost-effective scaling. Leadership: Mentor junior MLEs and Data Scientists on software engineering best practices, including version control, testing, and modularity. Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.