DevOps & MLOps Engineer

Job Title: DevOps MLOps Engineer

Location: Mumbai, Vikhroli

Job Type: Full-time

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 high-quality 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 pre-processing, 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 cloud-based environment (e.g., Azure) ensure optimal performance and cost-efficiency.

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 real-world 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 long-term solutions.

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

Required Qualifications and Experience:

Bachelor s 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: Full-time

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 high-quality 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 pre-processing, 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 cloud-based environment (e.g., Azure) ensure optimal performance and cost-efficiency.

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 real-world 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 long-term solutions.

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

Required Qualifications and Experience:

Bachelor s 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.