Associate - Data ML Engineer

Job Description:

Responsibilities

• Build and maintain scalable data pipelines and data processing workflows to support analytics and machine learning use cases.
• Develop and deploy machine learning models into production systems, ensuring reliability and scalability.
• Design and implement backend APIs to serve data products and ML outputs to downstream applications.
• Develop lightweight frontend applications, dashboards, or interfaces to enable business users to interact with data and models.
• Collaborate with data scientists to productionize models and integrate them into end-to-end solutions.
• Work with large-scale datasets across cloud-based data platforms and warehouses (e.g., Snowflake, Azure Synapse).
• Implement best practices in software engineering, including modular code design, testing, and version control.
• Utilize containerization (Docker) and CI/CD pipelines for efficient deployment and scaling of applications.
• Optimize performance of data pipelines, APIs, and user-facing applications.
• Translate business requirements into technical solutions and support stakeholders with data-driven tools and insights.

Qualification & Experience     

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related technical fields.
• 2–5 years of experience in data engineering, ML engineering, or full-stack development roles.
• Strong programming skills in Python and SQL, with experience building data pipelines and backend services.
• Hands-on experience with machine learning model integration and deployment in production environments.
• Experience developing backend APIs using frameworks such as FastAPI, Flask, or Django.
• Exposure to frontend technologies (e.g., React, NextJs or similar) to build user-facing applications and dashboards.
• Experience working with cloud platforms (AWS/Azure/GCP) and modern data architectures (data lakes, warehouses).
• Familiarity with CI/CD pipelines, Docker, and version control (Git).
• Understanding of data modeling, ETL processes, and scalable system design.
• Ability to work across the stack—from data pipelines to APIs to front-end interfaces.
• Good-to-have: Proficiency in Excel and PowerPoint with ability to support business communication and storytelling.
• Good-to-have: Prior exposure to consulting, analytics use cases, or Consumer Products domain.