Description
We are seeking a skilled ML Ops Engineer to join our team and drive the operationalization of machine learning models into production. The ideal candidate will have a strong background in machine learning pipelines, cloud platforms, and DevOps practices, ensuring that AI solutions are scalable, reliable, and secure.
Key Responsibilities
- Design and implement robust and scalable ML pipelines on Azure (using Azure ML, Azure Data Factory, Azure DevOps, etc.)
- Automate model training, validation, deployment and retraining workflows.
- Manage and monitor models in production (versioning, drift detection, retraining triggers).
- Implement CI/CD pipelines tailored for ML workloads.
- Exposure on Integrating LLMs into applications with frameworks like LangChain.
- Implementation of Orchestration framework for building and managing agentic System.
- Collaborate with Data Science and Engineering teams to ensure smooth integration of models with applications and APIs.
- Handle data ingestion and transformation pipelines using Azure Data Lake, Databricks, or Synapse.
- Apply best practices in security, performance, and cost optimization in Azure cloud environments.
- Troubleshoot and resolve deployment and model performance :
- Bachelors in Engineering
- 5+ yrs Experience in DevOps + MLOps
- Experience in deploying ML models
(ref:hirist.tech)
