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Mantra Softech - MLOps Engineer - CI/CD Pipeline

0 years

0 Lacs

ahmedabad gujarat india

Posted:16 hours ago| Platform: Linkedin logo

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Skills Required

pipeline learning automate model deployment monitoring scaling code data security compliance ml resolve logging ai flask fastapi android server onnx quantization programming python tensorflow pytorch aws azure devops docker kubernetes jenkins gitlab terraform ansible spark hadoop stack database management sql nosql airflow algorithms mlflow

Work Mode

On-site

Job Type

Full Time

Job Description

Key Responsibilities

  • Develop and maintain CI/CD pipelines for machine learning models.
  • Automate model deployment, monitoring, and scaling processes.
  • Implement and manage version control for code, data, and models.
  • Ensure data quality, security, and compliance throughout the ML lifecycle.
  • Collaborate with data scientists, engineers, and other stakeholders.
  • Optimize infrastructure for ML workloads.
  • Troubleshoot and resolve issues in production ML systems.
  • Implement logging, monitoring, and alerting for ML pipelines.
  • Manage and optimize cloud resources for ML/AI workloads.
  • Facilitate knowledge sharing and best practices across teams.
  • Mentor peer developers on MLOps / AIOps and DevOps.
  • Automate workflows for multi-model deployment on servers and embedded systems.
  • Develop and manage APIs for serving multiple deep learning models efficiently using Flask, FastAPI, or similar.
  • Optimize and convert models for embedded/PC/Android/Server deployment (e.g., TFLite, ONNX, .NEF) with quantization and Technical Competencies :
  • Programming languages : Python or similar.
  • ML frameworks : TensorFlow, PyTorch, scikit-learn.
  • Cloud platforms : AWS (preferred), Azure, or GCP.
  • DevOps tools : Docker, Kubernetes, Jenkins, GitLab CI, Azure DevOps.
  • Infrastructure as Code : Terraform, Ansible, CloudFormation.
  • Big data technologies : Spark, Hadoop, Kafka.
  • Monitoring and logging tools : ELK stack, Prometheus, Grafana.
  • Version control : Git.
  • Database management : SQL and NoSQL databases.
  • Data pipeline tools : Airflow, Kubeflow or similar.
  • CI/CD methodologies and tools for ML workflows.
  • Model serving : Flask, FastAPI, TorchServe, TensorFlow Serving.
  • Understanding of ML algorithms and model performance metrics.
  • Knowledge of data privacy and security best practices.
  • Familiarity with MLOps principles and tools : MLflow, DVC, Weights & Biases.
(ref:hirist.tech)

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