AI / ML Engineer II / III

Nielsen; Bengaluru; On-site; INR 1,400,000 - 2,400,000; Full time

Job summary

Nielsen in Bengaluru seeks a hands-on Machine Learning Engineer to design and build end-to-end ML systems powering intelligent products. You will own complex projects from conception to deployment, making critical design decisions and mentoring others.

The role requires deploying E2E ML pipelines, strong Python/SQL skills, and experience with distributed computing frameworks like Apache Spark, plus MLOps. You will collaborate with product managers and stakeholders to deliver robust, scalable

Qualifications

• Master’s or Bachelor's in Engineering, Mathematics, Statistics or related field.
• Proven track record shipping production ML systems.
• Deep expertise in classification, regression, anomaly detection, boosted models, deep learning and simulations with large data.
• Strong ML, data engineering and MLOps experience.

Responsibilities

• Own lifecycle of ML systems from problem formulation to deployment and monitoring.
• Make architectural decisions to keep ML systems scalable, reliable and efficient.
• Define technical roadmaps and plans for projects.
• Optimize data pipelines and ML models for performance.
• Mentor junior engineers and lead technical standards.
• Serve as technical point of contact with product managers and stakeholders.
• Foster a learning culture and advance audience measurement methodologies.

Education

Master’s or Bachelor’s degree in Engineering, Mathematics, Statistics or a related field

Tools

Python

SQL

Apache Spark

Airflow

MLOps

Job description

Company Description

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

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.

Qualifications

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.