MLops Engineer( 5 Yrs Hyderbad)

3 days ago

Bengaluru, Karnataka, India Orbion Infotech Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Job Description
MLOps Engineer

Position: MLOps Engineer

Experience Level: 3+ years

About Us
Celebal Technologies is a leading Solution Service company that provide Services the field of Data

Science, Big Data, Enterprise Cloud & Automation. We are at the forefront of leveraging cuttingedge technologies to drive innovation and enhance our business processes. As part of our

commitment to staying ahead in the industry, we are seeking a talented and experienced Data

& AI Engineer with strong Azure cloud competencies to join our dynamic team.

Job Summary
We are seeking a highly skilled Databricks MLOps Engineer to join our data and AI engineering

team. The ideal candidate will have strong hands-on experience with the Databricks ecosystem

and a proven track record of deploying, operationalizing, and maintaining machine learning

models at scale. You will work closely with data scientists, data engineers, and business

stakeholders to build robust, automated, and reliable ML pipelines in a production

environment.

Key Responsibilities

  • Databricks Platform Management
  • Work extensively with Databricks Workspaces, Jobs, Workflows, Unity Catalog, Delta

Lake, and MLflow for experiment tracking and model lifecycle management.

  • Manage and optimize Databricks clusters, compute resources, and workspace

permissions.

  • End-to-End ML Lifecycle (MLOps)
  • Implement and manage the complete ML lifecycle including model training, versioning,

deployment, monitoring, and retraining.

  • Design and support model deployment strategies such as A/B testing, blue-green

deployments, and canary releases.

  • Programming & Development
  • Develop scalable ML and data pipelines using Python (pandas, scikit-learn,

PyTorch/TensorFlow), PySpark, and SQL.

  • Maintain code quality through Git-based version control, reviews, and automated tests.
  • Cloud & Infrastructure
  • Work across cloud environments (AWS/Azure/GCP) to deploy and manage ML

infrastructure.

  • Implement and maintain Infrastructure as Code using Terraform.
  • Build and manage containerized ML workloads using Docker or Kubernetes.
  • CI/CD & Automation
  • Create and optimize CI/CD pipelines using Jenkins, GitHub Actions, or GitLab CI for ML

workflows.

  • Automate data validation, feature generation, model training, and deployment

pipelines.

    1. Monitoring & Observability
  • Configure and implement monitoring solutions using Databricks Lakehouse Monitoring

for data quality, model performance, model drift, and inference pipelines.

  • Integrate model explainability tools including SHAP and LIME.
  • Feature Engineering & Optimization
  • Build and manage features with Databricks Feature Store.
  • Run distributed training and hyperparameter tuning using Optuna, Ray Tune, or similar

tools.

  • Collaboration & Documentation
  • Work cross-functionally with data scientists, ML engineers, DevOps teams, and business

units.

  • Create clear, maintainable documentation for pipelines, processes, and systems.
  • Mentor junior engineers and contribute to team best p

Skills: mlops,ml,pipelines,azure

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