Mlops Engineer

Role & responsibilities

Job Title: MLOps Engineer

About the Role

MLOps Engineer

You will work closely with data scientists, data engineers, and DevOps teams to streamline the end-to-end ML lifecyclefrom model development to production deployment and continuous monitoring.

Key Responsibilities

• Design, implement, and maintain

  MLOps pipelines

  for ML model deployment and monitoring.
• Collaborate with data scientists to package and deploy ML models within

  Databricks

  environments.
• Develop scalable

  data processing pipelines

  using PySpark and SQL.
• Implement

  CI/CD pipelines

  for ML workflows using

  Jenkins

  , and ensure code quality using

  SonarQube

  .
• Integrate ML workflows with

  Git

  (version control) and

  Jira

  (project management).
• Automate data ingestion, feature engineering, model training, evaluation, and deployment processes.
  
• Monitor deployed models for

  performance drift

  , accuracy, and operational issues.
• Troubleshoot, optimize, and refactor ML pipelines for improved reliability and efficiency.
  
• Enforce

  coding standards, security best practices, and compliance

  across ML workflows.
• Work with

  Lakehouse architecture

  for model monitoring and feature store integration.
• Orchestrate ML jobs using

  Jenkins

  .

Required Skills & Qualifications

• 3+ years of experience as an

  MLOps Engineer

  , Data Engineer, or similar role.
• Strong expertise in

  Databricks

  for model deployment and data engineering.
• Proficiency in

  Python

  and

  PySpark

  for ML workflow automation.
• Solid

  SQL

  skills for structured data processing.
• Hands-on experience with

  CI/CD tools

  , especially

  Jenkins

  .
• Experience using

  SonarQube

  for code quality assessments.
• Familiarity with

  Git

  for version control and

  Jira

  for project/task management.
• Strong understanding of the

  ML model lifecycle

  (training validation deployment monitoring).
• Excellent problem-solving and collaboration skills.
  

Preferred Skills

• Experience working with

  AWS, Azure, or GCP

  for cloud-based ML solutions.
• Exposure to

  MLflow

  for experiment tracking and model registry.
• Understanding of

  security best practices

  for ML pipelines.