AI / ML Engineer II / III

Nielsen Sports; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Nielsen Sports in Bengaluru, India is seeking a hands-on Machine Learning Engineer to design and build end-to-end systems powering intelligent products. Lead projects from conception to deployment, mentor peers, and make critical design decisions to drive scalable ML solutions.

Ideal candidates have a track record shipping production ML systems, deep expertise in ML and MLOps, and strong Python/SQL skills, with experience in Spark and Airflow to manage large datasets and pipelines.

Qualifications

• Master's or Bachelor's degree in Engineering, Mathematics, Statistics or a related field.
• Proven track record of owning and shipping production machine learning systems.
• Deep expertise in classification models, regression models, anomaly detection, boosted models, deep learning and simulation problems, particularly with large datasets.
• Deep expertise in ML concepts, data engineering, and MLOps practices.
• Proficiency in Python and SQL.
• Must have deployed E2E ML systems by automating training and inference pipelines.
• Hands-on experience with distributed computing frameworks like Apache Spark.
• Experience with workflow orchestration tools (e.g., Airflow) and MLOps.

Responsibilities

• Own the complete lifecycle of machine learning systems, including problem formulation, data pipeline design, model development, deployment, and monitoring.
• Make key architectural and design decisions to ensure our ML systems are scalable, reliable, and efficient.
• Navigate project requirements, creating clear plans of action and defining technical roadmaps.
• Optimize complex data pipelines and ML models for performance.
• Mentor junior engineers on the team, fostering their growth and ensuring high technical standards.
• Act as the technical point of contact for your projects, managing communication with product managers and other stakeholders.
• Cultivate a team environment focused on continuous learning, where innovative audience measurement methodologies are developed and refined through collaborative effort.

Skills

Machine learning

Deep learning

Data engineering

MLOps

Python

SQL

Distributed computing

Education

Master's or Bachelor's in Engineering/Math/Stats

Tools

Apache Spark

Airflow

Job description

About the Role: As a Machine Learning Engineer, you will take a leading role in designing and building the end-to-end systems that power intelligent products at Nielsen. You will own complex projects from conception to deployment, making critical design and architectural decisions. This is a high-impact role for a hands-on engineer who can handle ambiguity, mentor others, and deliver robust, scalable ML solutions.

Responsibilities

• Own the complete lifecycle of machine learning systems, including problem formulation, data pipeline design, model development, deployment, and monitoring.
• Make key architectural and design decisions to ensure our ML systems are scalable, reliable, and efficient.
• Navigate project requirements, creating clear plans of action and defining technical roadmaps.
• Optimize complex data pipelines and ML models for performance.
• Mentor junior engineers on the team, fostering their growth and ensuring high technical standards.
• Act as the technical point of contact for your projects, managing communication with product managers and other stakeholders.
• Cultivate a team environment focused on continuous learning, where innovative audience measurement methodologies are developed and refined through collaborative effort.

Basic Qualifications

• Master s or Bachelor s degree in Engineering, Mathematics, Statistics or a related field.
• A proven track record of owning and shipping production machine learning systems.
• Deep expertise in classification models, regression models, anomaly detection, boosted models, deep learning and simulation problems, particularly with large datasets.
• Deep expertise in ML concepts, data engineering, and MLOps practices.
• Proficiency in Python and SQL.
• Must have deployed E2E ML systems by automating training and inference pipelines.
• Hands-on experience with distributed computing frameworks like Apache Spark.
• Experience with workflow orchestration tools (e.g., Airflow) and MLOps.