Overview

Responsibilities:
  • Design, develop, and deploy machine learning models and pipelines to solve complex business problems
  • Apply expertise in machine learning, optimization, and software engineering to create scalable AI solutions
  • Collaborate with technical and business stakeholders to ensure seamless integration of ML models into production systems
  • Guide and mentor junior engineers, advocate for engineering best practices, and contribute to the recruitment of technical talent
  • Build and optimize data pipelines, ETL processes, and model-serving frameworks
  • Drive innovation through effective problem-solving and identification of opportunities to leverage machine learning
  • Monitor, debug, and enhance model performance and reliability in production environments
Required Qualifications:
  • Bachelor’s degree in computer science, engineering, statistics, mathematics, or a related field (advanced degree preferred)
  • 5+ years of hands-on experience building and deploying machine learning models in production
  • Strong software engineering skills, including experience with Python, version control systems (e.g., Git), and ML frameworks (e.g., TensorFlow, PyTorch, or Scikit-learn)
  • Proficiency in building and optimizing scalable ETL pipelines and working with job-scheduling systems
  • Extensive experience with cloud platforms (e.g., AWS, GCP, Azure) and managing large-scale data processing
  • Expertise in deploying ML models using modern tools (e.g., Docker, Kubernetes, CI/CD pipelines)
  • Experience in causal inference and designing experiments is a plus
  • Strong understanding of MLOps principles and tools to streamline model deployment and lifecycle management
  • Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders
Note:

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