Machine Learning Engineer

JOB DESCRIPTION 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. JOB 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; Experience with API development using FastAPI, Flask, or similar frameworks would be a plus; 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; 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; Ability to write clean, maintainable, and documented code; Ability to work independently and collaboratively; Excellent knowledge of Armenian, Russian, and English languages.