Title: Machine Learning Engineer- ML Platform & Automation

Experience: 4-8 Years

Location- Anywhere (Must come to Bangalore to pick up Laptop )

Work Mode: Remote

Role Overview:

We are looking for a Machine Learning Engineer with strong experience in ML platform engineering, data pipelines, model evaluation, and automated model retraining. The ideal candidate will build scalable and automated frameworks that support the end-to-end machine learning lifecycle, from data preparation and model training through evaluation and retraining.

The role will involve working with Python, PySpark, ML evaluation frameworks, pipeline automation, and Walmart's internal Element ML platform.

Key Responsibilities:

- Build and maintain scalable machine learning pipelines for model training, evaluation, and retraining.
- Develop automated model evaluation frameworks using metrics such as nDCG, MRR, MAP@K, Precision@K, and Recall@K.
- Develop and maintain auto-retraining frameworks to enable models to retrain based on defined data, performance, or business triggers.
- Build large-scale data processing pipelines using Python and PySpark.
- Automate data preparation, feature generation, model training, evaluation, and other ML lifecycle processes.
- Integrate ML workflows with Element, Walmart's internal ML platform.
- Develop reliable and reusable pipeline components for production ML workflows.
- Implement monitoring and validation mechanisms to ensure data and model quality.
- Automate model comparison and validation before promoting new models to production.
- Work with Data Scientists, ML Engineers, Data Engineers,



and Product teams to productionize machine learning solutions.
- Troubleshoot and optimize ML pipelines for performance, scalability, and reliability.
- Improve existing ML workflows by reducing manual intervention and increasing automation.

Required Skills : Python + PySpark + ML pipelines + automated retraining + model evaluation + ML platform/MLOps

- Strong hands-on experience with Python.
- Strong experience with PySpark / Apache Spark and large-scale data processing.
- Experience building production machine learning pipelines.
- Experience with automated model training and retraining frameworks.
- Experience with offline model evaluation and evaluation metrics such as:
- nDCG / nDCG@K
- MRR / MRR@K
- MAP / MAP@K
- Precision@K / Recall@K
- Robust understanding of ML lifecycle and model productionization.
- Experience with pipeline automation and workflow orchestration.
- Experience working with an ML platform or equivalent ML infrastructure.
- Strong data engineering fundamentals.

Preferred Skills:

- Experience with Element ML Platform or similar enterprise ML platforms.
- Experience with Airflow, Concord, Kubeflow, MLflow, Databricks, SageMaker, Vertex AI, or Azure ML.
- Experience with automated model deployment and model monitoring.
- Experience with model versioning and experiment tracking.
- Experience building reusable ML platform components.
- Experience with search, recommendation, ranking, or information retrieval systems.
- Experience working with large-scale e-commerce or marketplace datasets.
- Familiarity with CI/CD and software engineering best practices for ML systems.

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📌 Machine Learning Engineer (India)
🏢 Lean IT
📍 India