Mlops Engineer / Devops Engineer

6 - 11 years

7 - 11 Lacs

Posted:3 weeks ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

  • Programming & Development
  • Python: Advanced proficiency in Python development
  • Software Design: Ability to create maintainable, well-tested code with proper error handling
  • API Development: Experience creating RESTful services and endpoints

DevOps & Infrastructure

  • Containerization: Docker image building and optimization
  • Package Management: Creating and maintaining Python packages and dependencies
  • Orchestration: Experience with Kubernetes and/or RunAI .
  • CI/CD: GitHub Actions workflow design and implementation .
  • Cloud Platforms: GCP experience, particularly with tools like Cloud Batch or similar services .
  • Resource Management: GPU allocation and workload optimization

Infrastructure Automation

  • Workflow Pipelines: Design and implementation of automated data workflows (processing, evaluation, deployment) .
  • Artifact Management: Experience with versioning, storage, and metadata tracking for models or data artifacts
  • Data Versioning: Implementing dataset versioning and change management .
  • Format Conversion: Knowledge of artifact optimization and format conversion (e.g., ONNX, TFLite, TensorRT, SNPE) .

Testing & Quality Assurance

  • Automated Testing: Unit, integration, and E2E test implementation for software systems
  • Performance Evaluation: Metrics calculation and performance benchmarking

Additional Technologies [Optional]

  • Configuration Systems: Experience with Hydra or similar configuration frameworks
  • Experiment Tracking: Familiarity with W&B, MLflow, or similar tracking tools
  • Image Processing: Understanding of computer vision workflows and image handling tasks

Current Projects & Responsibilities

  • Design and implement end-to-end deployment and monitoring pipelines .
  • Create automated workflows for data processing, quantization, conversion, and evaluation .
  • Develop version-controlled registries with comprehensive metadata capabilities .
  • Build systems for automated testing across multiple target architectures .
  • Implement robust data versioning and dataset management solutions .
  • Maintain CI/CD pipelines for software and system lifecycle management .
  • Optimize Docker environments for development and deployment.
  • Develop Python libraries and APIs for internal consumption

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