Cloud Based ML Workflow Engineer

5 days ago

Hyderabad, Telangana, India beBeeMachineLearning Full time ₹ 20,00,000 - ₹ 25,00,000

Job Overview

We are seeking an experienced Sr. MLOps Specialist with deep expertise in cloud-based ML pipelines to design, build, and maintain scalable, secure, and automated ML workflows. The successful candidate will play a key role in bridging the gap between data science and engineering teams, driving production readiness of ML models, and ensuring efficient lifecycle management.

Key Responsibilities:

  • Pipeline Design & Implementation: Design and implement robust MLOps pipelines for ML using Amazon SageMaker Pipelines, CodePipeline, Step Functions, etc.
  • CICD Pipeline Management: Build and maintain CI/CD pipelines for ML using AWS services like S3, Lambda, ECR, CloudWatch, etc.
  • Automation: Automate model training, evaluation, deployment, and monitoring processes.
  • Infrastructure & Cloud Management: Use Infrastructure-as-Code (IaC) tools to manage reproducible environments and architect scalable ML infrastructure using AWS services.
  • Monitoring, Logging & Observability: Implement model and data monitoring with SageMaker Model Monitor, CloudWatch, or third-party tools and set up logging, alerts, and dashboards to ensure model health and performance.
  • Governance & Compliance: Manage model registries, lineage tracking, and audit logging to support reproducibility and regulatory compliance.

Required Skills & Qualifications:

  • Cloud-Based ML Expertise: 8+ years in software engineering, DevOps, or ML Engineering with a focus on cloud-based ML pipelines.
  • AWS Services: Strong experience with Amazon Web Services (AWS), especially Amazon SageMaker, S3, Lambda, Step Functions, CodePipeline, ECR, CloudWatch.
  • Programming Languages: Proficiency in Python, Bash, and scripting for automation.
  • CI/CD Tools: Familiarity with CI/CD tools like Jenkins, GitHub Actions, CodeBuild, etc.
  • Container Orchestration: Experience with Docker and container orchestration in AWS.
  • ML Lifecycle: Understanding of ML lifecycle, including feature engineering, training, deployment, and monitoring.
  • Data Versioning & Model Tracking: Experience with data versioning and model tracking tools.

Preferred Qualifications:

  • AWS Certification: AWS Certification (e.g., AWS Certified Machine Learning – Specialty, Solutions Architect – Professional).
  • ML Frameworks: Knowledge of ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Multi-Environment Deployment: Experience with multi-environment deployment (dev/test/prod) for ML workflows.
  • Data Privacy Laws: Familiarity with data privacy laws and model governance frameworks.

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