Project description

We are seeking a highly skilled Senior MLOps Engineer / Data Scientist with a solid background in the Retail industry and Order-to-Cash (O2C) domains. The ideal candidate brings extensive development experience, including a deep foundation in programming and automation. In this role, you will bridge the gap between data science and production engineering. You will design, build, and maintain end-to-end Machine Learning pipelines. You will leverage Snowflake ML and Python to deploy scalable models. You will also use Azure DevOps for robust CI/CD automation. Additionally, you will translate complex data into actionable business insights using Power BI.
Responsibilities

Key Responsibilities

End-to-End MLOps: Design, deploy, and monitor scalable ML pipelines from data ingestion to model deployment and retraining.

Snowflake ML Development: Utilize Snowpark, Snowflake Cortex AI, and Model Registry to build and manage in-data-warehouse machine learning solutions.

Pipeline Automation: Build and maintain CI/CD pipelines using Azure DevOps for seamless,



automated model deployment and testing.

Domain Analytics: Apply ML models to optimize the Order-to-Cash (O2C) lifecycle, improving cash application, billing efficiency, and credit risk assessments.

Retail Solutions: Deliver data-driven solutions for retail use cases, including demand forecasting, inventory management, and customer analytics.

Business Intelligence: Create interactive Power BI dashboards and data models to translate complex ML outputs into clear executive insights.

Required Skills & Qualifications (Mandatory)

Python Expertise: Minimum of 5+ years of hands-on, professional Python development experience writing clean, production-grade code.

Snowflake Ecosystem: Hands-on experience with Snowflake ML tools (Snowpark, Cortex AI, or Feature Store).

DevOps Tools: Proven experience with Azure DevOps, Git, and automated CI/CD workflows.

BI Tools: Strong knowledge or experience with Power

📌 Senior MLOps Engineer (India)
🏢 Luxoft
📍 India