MLOps Engineer (6-12 yrs)

Job Title : MLops Engineer

Location : Bangalore

Job Type : Full-Time

Experience : 6-12 yrs

Responsibilities :

- Design, implement, and manage infrastructure solutions that support large-scale analytics and machine learning workflows.

- Work with Kubeflow, MLflow, AWS Sagemaker, SLURM, HPC, and Vertex AI to deploy, monitor, and scale machine learning models and data processing tasks.

- Collaborate with data scientists and engineers to ensure that analytics tools are optimized for performance and cost.

- Monitor system performance, troubleshoot issues, and implement solutions to ensure high availability and reliability.

- Stay up to date with industry trends and advancements in analytics infrastructure and tools

Detailed JD of MLops :

- Design, implement, and manage infrastructure solutions that support large-scale analytics and machine learning workflows.

- Work with Kubeflow, MLflow, AWS Sagemaker, SLURM, HPC, and Vertex AI to deploy, monitor, and scale machine learning models and data processing tasks.

- Collaborate with data scientists and engineers to ensure that analytics tools are optimized for performance and cost.

- Monitor system performance, troubleshoot issues, and implement solutions to ensure high availability and reliability.

- Stay up to date with industry trends and advancements in analytics infrastructure and tools

- Automate the deployment, testing, and monitoring of machine learning models in production environments, ensuring continuous integration and continuous delivery (CI/CD) pipelines.

- Collaborate with cross-functional teams to define infrastructure and scalability requirements for machine learning and data science projects.

- Develop and maintain tools to track and manage model performance, data drift, and model versioning in production.

- Ensure security best practices are applied to machine learning workflows and data handling, including access control and data privacy compliance.

- Optimize cloud resource utilization, managing cost-effectiveness while scaling and maintaining large-scale machine learning applications.

- Implement model retraining pipelines and orchestrate workflows for continuous model improvements based on evolving data.

- Support model monitoring frameworks and build dashboards for end-users to visualize model health, prediction accuracy, and performance metrics.

- Assist in selecting appropriate technologies and frameworks for machine learning infrastructure based on project requirements and scalability.

Mandatary Skills :

- MLOps,

- Grade ML system development,

- Orchestration,

- Pipelining,

- Monitoring