Overview

An ML Engineer will support the Marketing Department by building, improving, and operationalizing Machine Learning models, scoring workflows, and model-powered APIs. The role will focus on the engineering side of ML projects, including model training, evaluation, deployment readiness, automation, CI/CD, containers, monitoring, and model lifecycle management.

Responsibilities:
  • Study the best foreign experience and modern approaches in Machine Learning engineering, MLOps, model deployment, and ML system design
  • Develop, improve, and maintain Machine Learning models and model training workflows
  • Build reusable ML pipelines for data preparation, training, validation, scoring, and retraining
  • Develop batch scoring workflows and model-powered APIs when needed
  • Support model evaluation, benchmarking, testing, and performance monitoring
  • Support model lifecycle management, including experiment tracking, model versioning, model registry usage, reproducibility, and monitoring
  • Package ML solutions for practical use and deployment readiness using Docker or similar containerization approaches
  • Support CI/CD practices for ML projects to improve reliability, automation, and maintainability
  • Support integration of ML outputs into applications, analytical tools, dashboards, reports, or business processes
  • Implement code quality, testing, logging, documentation, and version control practices in ML projects
  • Support stable deployment and monitoring of developed models in available technical environments
Required Qualifications:
  • Bachelor’s Degree in a technical related field; computer science, engineering, mathematics, or statistics background is a plus
  • Strong programming skills in Python
  • Good knowledge of SQL and data manipulation
  • Good knowledge of Machine Learning algorithms and model evaluation methods
  • Experience with Python ML-related packages such as pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow or similar
  • Experience with building training, validation, scoring, and retraining workflows
  • Practical understanding of Docker-based packaging and deployment of Python/ML solutions
  • Understanding of CI/CD principles for ML or software projects
  • Knowledge of experiment tracking, model versioning, and model registry tools such as MLflow or similar
  • Knowledge of model monitoring, logging, testing, and reproducibility practices
  • Ability to write clean, maintainable, and documented code
  • Ability to work independently and collaboratively
  • Excellent knowledge of Armenian, Russian, and English languages
Nice To Have:
  • Experience with API development using FastAPI, Flask, or similar frameworks would be a plus
  • Familiarity with containerization (Docker), workflow orchestration tools (like Airflow or Prefect), or local cluster management (Kubernetes) for ML pipelines would be a plus
  • Knowledge of LLMs, RAG, or AI application development would be a plus
Technologies:
  • Python
  • SQL
  • pandas
  • NumPy
  • scikit-learn
  • XGBoost
  • LightGBM
  • CatBoost
  • PyTorch
  • TensorFlow
  • FastAPI
  • Flask
  • Docker
  • MLflow
  • Airflow
  • Prefect
  • Kubernetes
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