Senior Data & AI Engineer

The MLOps Engineer is responsible for building| automating| deploying| monitoring| and maintaining Machine Learning (ML) models and platforms in production environments. The role bridges Data Science| Software Engineering| and DevOps practices to ensure scalable| reliable| and secure ML solutions. Key Responsibilities

• Design

  build

  and maintain end-to-end ML pipelines for training

  testing

  deployment

  and monitoring.

• Automate CI/CD processes for machine learning models and data workflows.

• Deploy and manage ML models on cloud platforms such as Azure

  AWS

  or Google Cloud Platform.

• Implement model monitoring

  performance tracking

  drift detection

  and retraining strategies.

• Collaborate with Data Scientists

  Data Engineers

  and Application Development teams to operationalize ML solutions.

• Manage containerization and orchestration using Docker and Kubernetes.

• Ensure security

  compliance

  scalability

  and reliability of ML platforms.

• Optimize infrastructure utilization and cloud costs for ML workloads.
• \Troubleshoot production issues and perform root cause analysis.

• Maintain documentation

  standards

  and best practices for MLOps processes.

• Strong programming skills in Python.

• Experience with ML frameworks such as TensorFlow

  PyTorch

  or Scikit-learn.

• Hands-on experience with CI/CD tools (Azure DevOps

  Jenkins

  GitHub Actions).

• Knowledge of Docker

  Kubernetes

  and Infrastructure as Code (Terraform

  ARM

  etc.).

• Experience with cloud platforms (Azure ML

  AWS SageMaker

  Google Cloud Platform Vertex AI).

• Familiarity with version control systems such as Git.

• Knowledge of monitoring and logging tools (Prometheus

  Grafana

  ELK

  Azure Monitor).

• Understanding of data engineering concepts and ETL pipelines.

Preferred Qualifications

• Bachelors or Masters degree in Computer Science

  Data Science

  Engineering

  or related field.

• Relevant certifications in Azure

  AWS

  Kubernetes

  or DevOps.

• Experience implementing enterprise-scale AI/ML solutions.