DevOps MLOps Engineer

Job Title: DevOps & MLOps Engineer

Location: Mumbai Vikhroli

Job Type: Fulltime

Position Summary: We are seeking a skilled and proactive DevOps & MLOps Engineer to join our dynamic team. The ideal candidate will bridge the gap between development and IT operations ensuring smooth deployment scalability and monitoring of our applications and infrastructure. You will play a pivotal role in delivering highquality software faster and more reliably. The role will also focus on managing and scaling machine learning workflows and models.

Job Responsibilities:

CI/CD Pipeline Development and Management:

Design build and maintain Continuous Integration/Continuous Deployment pipelines to streamline software delivery. Automate build test and deployment processes to enhance development efficiency.

Build pipelines for data preprocessing training validation and deployment of ML models. Ensure data and model versioning for reproducibility.

Infrastructure Automation:

Implement Infrastructure as Code (IaC) using tools like Terraform Ansible or Cloud Formation. Manage cloudbased environment (e.g. Azure) ensure optimal performance and costefficiency.

Solid understanding of machine learning workflows data engineering and model deployment. Expertise in tools like ML flow TFX and cloud ML services.

Monitoring and Incident Response:

Develop robust monitoring solutions using tools like Prometheus Grafana Datadog or Splunk. Respond to system issues ensuring high availability and minimal downtime.

Handling large and evolving datasets. Ensuring reproducibility of experiments and models. Monitoring and updating ML models in response to realworld changes.

Collaboration and Communication:

Work closely with development QA and IT teams to ensure seamless integration and deployment of applications. Advocate for DevOps best practices and foster a culture of continuous improvement.

Works closely with data scientists machine learning engineers and data engineers. Bridges the gap between data science and production systems.

Security and Compliance:

Implement security best practices in CI/CD pipelines and infrastructure. Collaborate with security teams to ensure compliance with organizational and industry standards.

Monitor model performance (e.g. drift detection accuracy degradation).Handle the unique challenges of model retraining and updating.

Performance Optimization:

Analyze and improve system performance scalability and reliability. Conduct root cause analysis of production errors and implement longterm solutions.

Handling large and evolving datasets. Ensuring reproducibility of experiments and models. Monitoring and updating ML models in response to realworld changes.

Required Qualifications and Experience:

Bachelors degree in Computer Science Engineering or a related field (or equivalent practical experience).

3 years of experience in a DevOps MLOps or related role.

Technical skills:

Strong expertise in CI/CD tools (e.g. Jenkins GitHub Actions).

Proficiency in at least one programming/scripting language (e.g. Python Go).

Solid understanding of containerization & orchestration tools like Docker and Kubernetes.

Familiarity with monitoring and logging tools (e.g. Prometheus ELK stack CloudWatch).

ML Platforms: MLflow TFX (TensorFlow Extended) SageMaker

Model Serving: TensorFlow Serving TorchServe ONNX Runtime.

Monitoring: Evidently AI WhyLabs or custom dashboards for model performance.

Knowledge of version control systems like Git and branching strategies.

Knowledge of DevSecOps practices & tools networking concepts and troubleshooting.

Familiarity with Agile and Scrum methoJob Title: DevOps & MLOps Engineer

Location: Mumbai Vikhroli

Job Type: Fulltime

Position Summary: We are seeking a skilled and proactive DevOps & MLOps Engineer to join our dynamic team. The ideal candidate will bridge the gap between development and IT operations ensuring smooth deployment scalability and monitoring of our applications and infrastructure. You will play a pivotal role in delivering highquality software faster and more reliably. The role will also focus on managing and scaling machine learning workflows and models.

Job Responsibilities:

CI/CD Pipeline Development and Management:

Design build and maintain Continuous Integration/Continuous Deployment pipelines to streamline software delivery. Automate build test and deployment processes to enhance development efficiency.

Build pipelines for data preprocessing training validation and deployment of ML models. Ensure data and model versioning for reproducibility.

Infrastructure Automation:

Implement Infrastructure as Code (IaC) using tools like Terraform Ansible or Cloud Formation. Manage cloudbased environment (e.g. Azure) ensure optimal performance and costefficiency.

Solid understanding of machine learning workflows data engineering and model deployment. Expertise in tools like ML flow TFX and cloud ML services.

Monitoring and Incident Response:

Develop robust monitoring solutions using tools like Prometheus Grafana Datadog or Splunk. Respond to system issues ensuring high availability and minimal downtime.

Handling large and evolving datasets. Ensuring reproducibility of experiments and models. Monitoring and updating ML models in response to realworld changes.

Collaboration and Communication:

Work closely with development QA and IT teams to ensure seamless integration and deployment of applications. Advocate for DevOps best practices and foster a culture of continuous improvement.

Works closely with data scientists machine learning engineers and data engineers. Bridges the gap between data science and production systems.

Security and Compliance:

Implement security best practices in CI/CD pipelines and infrastructure. Collaborate with security teams to ensure compliance with organizational and industry standards.

Monitor model performance (e.g. drift detection accuracy degradation).Handle the unique challenges of model retraining and updating.

Performance Optimization:

Analyze and improve system performance scalability and reliability. Conduct root cause analysis of production errors and implement longterm solutions.

Handling large and evolving datasets. Ensuring reproducibility of experiments and models. Monitoring and updating ML models in response to realworld changes.

Required Qualifications and Experience:

Bachelors degree in Computer Science Engineering or a related field (or equivalent practical experience).

3 years of experience in a DevOps MLOps or related role.

Technical skills:

Strong expertise in CI/CD tools (e.g. Jenkins GitHub Actions).

Proficiency in at least one programming/scripting language (e.g. Python Go).

Solid understanding of containerization & orchestration tools like Docker and Kubernetes.

Familiarity with monitoring and logging tools (e.g. Prometheus ELK stack CloudWatch).

ML Platforms: MLflow TFX (TensorFlow Extended) SageMaker

Model Serving: TensorFlow Serving TorchServe ONNX Runtime.

Monitoring: Evidently AI WhyLabs or custom dashboards for model performance.

Knowledge of version control systems like Git and branching strategies.

Knowledge of DevSecOps practices & tools networking concepts and troubleshooting.

Familiarity with Agile and Scrum methodologies.

Required Experience:

Senior IC