Machine Learning Engineer

Software Developer
Hyderabad, Chennai, Bengaluru
February 13, 2026
Full Time
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Job Description

We are seeking a Data Platform Engineer to support end-to-end machine learning workflows by building scalable data pipelines, enabling smooth model deployment, and ensuring reliable production integrations. This role sits at the intersection of data engineering and ML engineering, working closely with data scientists and platform teams to operationalize machine learning models efficiently.

Key Responsibilities

  • Collaborate with data scientists across the full ML lifecycle, including:
    • Data preparation
    • Feature engineering
    • Model training, evaluation, and scoring
  • Build and maintain automated data pipelines for ingestion, transformation, and model scoring using Python and SQL.
  • Support model deployment through CI/CD pipelines (e.g., Jenkins) and ensure seamless integration with production systems.
  • Develop scripts and tools for:
    • Model monitoring
    • Logging
    • Retraining workflows
  • Work with structured data from relational databases such as Amazon RDS and Amazon Redshift.
  • Analyze pipeline performance and model behavior to identify opportunities for:
    • Optimization
    • Refactoring
    • Reliability improvements
  • Contribute to the design and development of a feature store and standardized workflows to enable reproducible and scalable data science practices.

Required Skills & Experience

  • 1–3 years of hands-on experience in Python programming for data science or ML engineering tasks.
  • Solid understanding of machine learning workflows, including:
    • Model training
    • Validation
    • Deployment
    • Monitoring
  • Strong proficiency in SQL and working with structured datasets.
  • Experience with ETL pipelines and data transformation best practices.
  • Basic knowledge of CI/CD pipelines, particularly tools like Jenkins.
  • Strong analytical mindset with the ability to debug data and model-related issues.

Preferred Qualifications

  • Exposure to ML frameworks such as:
    • scikit-learn
    • XGBoost
    • LightGBM
  • Familiarity with ML lifecycle and orchestration tools (e.g., MLflow, Ray).
  • Experience working on cloud platforms, preferably AWS.
  • Knowledge of:
    • Data or model versioning tools
    • Feature engineering frameworks
  • Understanding of scalable data infrastructure and production ML systems.

Education

  • UG: B.Tech / B.E. in any specialization
  • PG: Any Postgraduate (optional)

Key Skills

Machine Learning, Python, SQL, CI/CD, Jenkins, Amazon Redshift, Data Pipelines, ETL, Feature Engineering, Model Deployment, AWS

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