Seller Operations Machine Learning Engineer

Job Title: AI / ML Specialist – Seller Operations

Location: Gurgaon, India

About noon

We’re building an ecosystem of digital products and services that power everyday life across the Middle East—fast, scalable, and deeply customer-centric. Our mission is to deliver to every door every day. We want to redefine what technology can do in this region, and we’re looking for an AI / ML Specialist who can help us move even faster.

noon’s mission: Every door, every day.

What you'll do:

Team noon has some of the fastest, smartest, and hardest-working people we've encountered. With a young, aggressive, and talented team, we're driving major missions forward.

An AI/ML Specialist will develop and deploy intelligent models to automate processes, predict seller behavior, and improve operational efficiency. The role involves building machine learning solutions for use cases such as seller risk, performance optimization, and anomaly detection. The role will collaborate with product, data, and operations teams to embed AI-driven insights into seller-facing systems and workflows.

Key Responsibilities:

You will:

• Design and develop ML models to solve Seller Ops problems such as seller scoring, risk detection, fraud prevention, demand forecasting, SLA prediction, and churn reduction.
• Apply NLP techniques to seller tickets, feedback, and communications to improve automation and resolution times.
• Build and deploy predictive and prescriptive models to optimize seller onboarding, performance, and compliance.
• Partner with Product and Ops teams to identify high-impact AI/ML use cases and define success metrics.
• Own the end-to-end ML lifecycle: data understanding, feature engineering, model training, evaluation, deployment, and monitoring.
• Work with Data Engineering teams to build scalable data pipelines and feature stores.
• Monitor model performance, bias, and drift;
  continuously improve model accuracy and reliability.
• Support experimentation and A/B testing to measure business impact of ML-driven solutions.
• Document models, assumptions, and decision frameworks for stakeholder understanding.

Requirements:

• 4–6 years of experience in Machine Learning / Applied AI / Data Science roles.
• Strong foundation in statistics, probability, and machine learning algorithms.
• Proficiency in Python and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch, etc.).
• Hands-on experience with SQL and working with large datasets.
• Experience deploying models in production environments (APIs, batch jobs, or real-time systems).
• Understanding of model evaluation, explain ability, and performance monitoring.
• Experience collaborating with cross-functional teams (Product, Ops, Engineering).
• Exposure to LLMs, GenAI, and prompt engineering for operational automation.
• Experience with ML Ops tools (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI).
• Familiarity with cloud platforms (AWS / GCP / Azure).

Who will excel?

• We’re looking for people with high standards, who understand that hard work matters.
• You need to be relentlessly resourceful and operate with a deep bias for action.
• We need people with the courage to be fiercely original.
• noon is not for everyone;
  readiness to adapt, pivot, and learn is essential.