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Machine Learning Engineer

Machine Learning Engineer

Cricviz

3 - 8 years

5 - 10 Lacs

mumbai

Posted:1 hour ago| Platform:

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Skills Required

computer science gcp analytical machine learning workflow gaming monitoring analytics python recruitment

Work Mode

Work from Office

Job Type

Full Time

Job Description

CricViz is looking for a Machine Learning Engineer to help build and operate the high-performance ML systems behind our industry-leading cricket intelligence products including WinViz, PitchViz, Player Impact, and more. You ll work at the intersection of data engineering, MLOps, and applied sports analytics, turning analytical Python workflows into scalable, production-ready pipelines. The role is embedded within the Data Science team, working closely with data scientists to translate analytical work into production-ready systems. This is a chance to apply your expertise to a world-class sports product that impacts how broadcasters, teams, and fans understand the game.

Responsibilities

Build and maintain robust, scalable ETL pipelines for batch and real-time cricket data.
Orchestrate workflows using tools like Dagster and Celery.
Support the full ML model lifecycle data preparation, feature generation, model execution, evaluation, and deployment.
Contribute to the design and implementation of feature engineering pipelines across products like WinViz, PitchViz, and Impact.
Implement observability, logging, monitoring, and retry logic to ensure reliability.
Participate in model evaluation and backtesting to ensure reliability before deployment.
Modernize and refactor legacy processes for maintainability and performance.
Contribute to shared infrastructure such as schema standards, deployment templates, and tooling.

Requirements

Degree in Computer Science, Engineering, or a related technical field.
3+ years in ML Engineering, Software Engineering, or Data Science.
Strong Python (pandas/numpy) and Git experience.
Familiarity with workflow orchestration (Dagster, Airflow, Prefect).
Experience with messaging/queuing systems (Celery, Kafka, RabbitMQ).
Understanding of the ML lifecycle and MLOps concepts.
Experience with Docker; cloud experience (AWS/GCP).
Strong problem-solving skills and ability to build reliable, maintainable pipelines.
Great communication and documentation habits.
Interest in sports analytics (particularly cricket).

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Machine Learning Engineer