MLOps Engineer

Noida - India

Monthly Salary: Not Disclosed

Posted on: 14 hours ago

Vacancies: 1 Vacancy

Job Summary

Role & Responsibilities

We are looking for a Senior MLOps Engineer with 8 years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS Airflow/MWAA Apache Spark Kubernetes (EKS) and automation of ML lifecycle workflows. You will work closely with data science data engineering and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

Design and manage cloud-native ML platforms supporting training inference and model lifecycle automation.
Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
Containerize and deploy ML workloads using Docker EKS ECS/Fargate and Lambda.
Develop CI/CT/CD pipelines integrating model validation automated training testing and deployment.
Implement ML observability: model drift data drift performance monitoring and alerting using CloudWatch Grafana Prometheus.
Ensure data governance versioning metadata tracking reproducibility and secure data pipelines.
Collaborate with data scientists to productionize notebooks experiments and model deployments.

Ideal Candidate

8 years in MLOps/DevOps with strong ML pipeline experience.
Strong hands-on experience with AWS:
Compute/Orchestration: EKS ECS EC2 Lambda
Data: EMR Glue S3 Redshift RDS Athena Kinesis
Workflow: MWAA/Airflow Step Functions
Monitoring: CloudWatch OpenSearch Grafana
Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
Expertise with Docker Kubernetes Git CI/CD tools (GitHub Actions/Jenkins).
Strong Linux scripting and troubleshooting skills.
Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

Masters degree in Computer Science Machine Learning Data Engineering or related field.

Perks Benefits and Work Culture

Competitive Salary Package
Generous Leave Policy
Flexible Working Hours
Performance-Based Bonuses
Health Care Benefits

Role & Responsibilities We are looking for a Senior MLOps Engineer with 8 years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS Airflow/MWAA Apache Spark Kubernetes (EKS) and automation of ML lifecycle work...

Role & Responsibilities

Key Responsibilities:

Design and manage cloud-native ML platforms supporting training inference and model lifecycle automation.
Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
Containerize and deploy ML workloads using Docker EKS ECS/Fargate and Lambda.
Develop CI/CT/CD pipelines integrating model validation automated training testing and deployment.
Implement ML observability: model drift data drift performance monitoring and alerting using CloudWatch Grafana Prometheus.
Ensure data governance versioning metadata tracking reproducibility and secure data pipelines.
Collaborate with data scientists to productionize notebooks experiments and model deployments.

Ideal Candidate

8 years in MLOps/DevOps with strong ML pipeline experience.
Strong hands-on experience with AWS:
Compute/Orchestration: EKS ECS EC2 Lambda
Data: EMR Glue S3 Redshift RDS Athena Kinesis
Workflow: MWAA/Airflow Step Functions
Monitoring: CloudWatch OpenSearch Grafana
Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
Expertise with Docker Kubernetes Git CI/CD tools (GitHub Actions/Jenkins).
Strong Linux scripting and troubleshooting skills.
Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education: