MLOps Engineer

Job Description
We are looking for an experienced, hands-on MLOps Engineer with strong expertise in Python and SQL and proven experience partnering with Data Scientists to productionize machine learning solutions.
The ideal candidate will have strong knowledge of the ML lifecycle, including model development, deployment, CI/CD, monitoring, and production support. This person should be able to effectively bridge the gap between Data Science experimentation and Production Engineering.
Required Skills
•    Strong hands-on experience as an MLOps Engineer.
•    Strong programming skills in Python.
•    Strong experience with SQL and data manipulation.
•    Experience working closely with Data Scientists to productionize ML models.
•    Strong understanding of the complete Machine Learning lifecycle.
•    Experience with ML model deployment and productionization.
•    Hands-on experience implementing CI/CD pipelines for machine learning solutions.
•    Experience with ML model monitoring, performance monitoring, and production support.
•    Ability to bridge Data Science experimentation and Production Engineering.
•    Strong experience with AWS SageMaker or equivalent AWS ML services.
•    Understanding of cloud-based ML infrastructure and deployment best practices.
•    Experience troubleshooting and supporting ML solutions in production.
Key Responsibilities
•    Partner with Data Scientists to move ML models from experimentation into production.
•    Design and implement scalable MLOps pipelines.
•    Automate model deployment and release processes using CI/CD.
•    Develop and maintain ML infrastructure and deployment workflows.
•    Implement monitoring for models, pipelines, applications, and infrastructure.
•    Support model versioning, deployment, and lifecycle management.
•    Collaborate with Data Science and Engineering teams to ensure reliable production deployments.
•    Improve the scalability, reliability, and automation of ML workflows.
•    Troubleshoot production issues and continuously improve MLOps processes.