ML Engineer III

Role Overview

• Build, train, and optimise Classical ML, Deep learning and Transformer-based models.
• Perform EDA, feature engineering, and model evaluation on large datasets.
• Use PySpark for large-scale data processing and feature computation.
• Productionize models using Databricks (MLflow, Model Registry, Model Serving).
• Implement batch and real-time inference workflows.
• Deploy and operate models on AWS-based environments.
• Monitor model performance and data drift in production.
• Collaborate with data engineering and platform teams.

Requirements

• 3–5 years of experience in Data Science / Applied ML
• Strong Python and SQL
• Solid understanding of ML algorithms and deep learning fundamentals.
• Hands-on experience with PySpark.
• Proven exposure to production ML systems.
• Good to Have
• Experience with Databricks Feature Store
• Docker / Kubernetes basics

Tech Stack

• AWS
• Docker
• Kubernetes
• PySpark
• Python
• SQL

Benefits

• Value Customers & Data
• Prioritize customers, use data-driven decisions, master KPIs, and leverage ideation and A/B testing to drive impactful outcomes.
• Inspire Extreme Ownership
• We embrace ownership, collaborate effectively, and take pride in every detail to ensure every game becomes a smashing success.
• Lead with Love
• We reject micromanagement and fear, fostering open dialogue, mutual growth, and a fearless yet responsible work ethic.
• Embrace change
• Change drives progress, and our strength lies in adapting swiftly and recognising when to evolve to stay ahead.
• Play the Big Game
• We think big, challenge norms, and innovate boldly, driving impactful results through fresh ideas and inventive problem-solving.