Senior Python Developer (IR445)

Nice to have skills

• Experience with Machine Learning (ML): deploying and managing models, creating inference pipelines, and ML Ops practices.
• Knowledge of ML platforms such as Airflow, SageMaker, Kubeflow, or MLFlow.
• Experience with distributed computing (e.g., SparkPySpark).
• Understanding of cloud ML deployment and model serving on platforms like AWS, Azure, or GCP.
• Experience with Large Language Models (LLMs), model finetuning, and RetrievalAugmented Generation (RAG) pipelines.
• Experience in developing enterprisegrade applications that leverage datadriven decisionmaking.

• Responsibilities

  • Develop, maintain, and optimize backend services and APIs using Python, ensuring high performance and scalability.
  • Design and implement data processing pipelines to handle large volumes of data efficiently.
  • Collaborate with crossfunctional teams to understand client needs and translate them into practical features.
  • Write clean, testable, and maintainable code, following best practices and coding standards.
  • Ensure code quality through code reviews, unit testing (using Pytest), and integration testing.
  • Implement and maintain CICD pipelines, and ensure smooth deployment processes.
  • Integrate and manage cloudbased solutions and infrastructure (e.g., AWS, Azure, GCP).
  • Utilize DevOps tools and practices for containerization and orchestration (Docker, Kubernetes).
  • Monitor application performance, identify bottlenecks, and implement improvements.
  • Participate in team meetings, providing updates on progress and discussing upcoming tasks.
  • Utilize issue tracking and version control systems (like Git) to maintain highquality software development processes.
  • Stay updated on the latest technologies and apply innovative solutions where applicable.

  • NicetoHave Responsibilities (ML Focus):

    • Develop, refine, and utilize ML engineering platforms and components as needed.
    • Establish and manage processes for data preparation, feature engineering, and prediction.
    • Closely monitor model performance and address any issues that arise.
    • Explore opportunities to integrate ML workflows into backend systems when it adds value.
    • Stay informed about ML Ops practices and maintain pipelines for training and inference.

    • Benefits

      • 35 absence days per year for worklife balance
      • Udemy courses of your choice
      • English courses with nativespeaker
      • Regular softskills trainings
      • Excellence Сenters meetups
      • Onlineoffline teambuildings