ML Engineer

ML Engineer

Role Overview

Design, develop, train, and optimize machine learning and deep learning models for production use. Build end-to-end ML pipelines and deploy scalable AI services that enable data-driven insights.

Responsibilities

• Develop and optimize ML/deep learning models for production environments
• Build end-to-end ML pipelines, including:
  • Data preprocessing
  • Feature engineering
  • Model training and evaluation
  • Deployment
• Collaborate with cross-functional teams to translate business requirements into AI/ML solutions
• Deploy, monitor, and maintain models in production
• Build scalable AI services and APIs using Python and modern ML frameworks
• Document model architectures, development processes, and technical solutions

Requirements

• Strong expertise in Python, ML algorithms, and data processing
• Experience designing, developing, deploying, and maintaining ML models and AI applications
• Ability to work closely with data scientists, software engineers, and business stakeholders
• MLOps experience (e.g., versioning, automation, model monitoring, continuous improvement)
• Experience improving model inference performance for scalability and reliability
• Experience working with structured and unstructured data

Nice-to-haves

• Not explicitly stated (emphasis on modern ML frameworks and scalable production AI services)