Senior Machine Learning Operations Engineer

• Design and implement Machine Learning infrastructure within AWS Sagemaker for scalable, secure, and automated model training, deployment, and monitoring pipelines. 
• Set up and maintain MLFlow for model tracking, experiment management, and governance with a focus on production-readiness and reproducibility. 
• Integrate data sources and pipeline orchestration using Apache Airflow to feed data into SageMaker model training workflows. 
• Build CI/CD pipelines for robust testing, linting, vulnerability scanning, and seamless model deployment. 
• Automate and manage infrastructure provisioning using Terraform, ensuring repeatable and compliant infrastructure-as-code practices. 
• Implement monitoring and observability, enabling fine-grained insights into pipeline performance, resource usage, and anomaly detection. 
• Collaborate closely with Data Scientists to streamline experimentation workflows, optimize pipeline runtimes, and scale computational resources. 

• 4+ years of experience in DevOps or MLOps roles with a strong focus on ML model lifecycle automation and infrastructure scalability. 
• Proficiency with AWS SageMaker and associated AWS services such as S3, IAM, Secrets Manager, and CloudWatch. 
• Practical experience with ML pipeline tooling such as MLFlow for model tracking and Apache Airflow for workflow orchestration. 
• Solid skills in scripting and automation using Python, Bash, and YAML. 
• Experience building and maintaining CI/CD workflows. 
• Experience in Azure DevOps.
• Familiarity with Infrastructure as Code principles and hands-on experience with Terraform. 
• Comfortable working in cross-functional teams with Data Scientists, helping bridge experimentation and production environments. 

Will be a plus:

• Experience with data lake formats for managing large-scale tabular data in ML workflows. 
• Knowledge of advanced resource optimization and auto-scaling strategies. 
• Prior work in highly regulated domains such as identity verification, where traceability, explainability, and compliance is critical. 
• Understanding of GenAI/LLM workflows and how to enable scalable infrastructure to support those pipelines. 
• Familiarity with FastAPI for building RESTful ML service interfaces.