Senior Machine Learning Engineer (AWS)

TL;DR

Senior Machine Learning Engineer (AWS): Developing and deploying advanced ML solutions for survey respondent matching and data quality with an accent on MLOps practices and production reliability. Focus on building scalable ML pipelines on AWS, mentoring team members, and driving technical decisions across the full model lifecycle.

Location: Must be based in Debrecen, Hungary

Company

hirify.global is a global leader in first-party data and insights, providing data-driven solutions for market research and decision-making.

What you will do

• Develop, train, and optimize machine learning models using gradient-boosted frameworks.
• Design, build, and maintain end-to-end ML pipelines on AWS.
• Own the full model lifecycle from experimentation to production deployment and monitoring.
• Implement robust monitoring, logging, and alerting for deployed models.
• Build and integrate real-time and batch inference systems and APIs.
• Mentor team members and drive technical standards for ML development and MLOps.

Requirements

• 5+ years of experience building, deploying, and scaling ML models in production.
• Proficiency in Python and ML libraries like LightGBM, Scikit-learn, NumPy, and Pandas.
• Strong MLOps expertise including CI/CD for ML, deployment, and monitoring.
• Hands-on experience with AWS services such as SageMaker, S3, Fargate, and Lambda.
• SQL proficiency with experience querying large datasets in Snowflake or similar.
• Ability to work autonomously and mentor others in best practices.

Nice to have

• Java experience for application layer integration.
• Experience with infrastructure as code tools like Terraform or CDK.
• Familiarity with containerization and orchestration (Docker, ECS).
• Experience with experiment tracking tools like MLflow or Weights & Biases.
• Knowledge of transformer architectures, fine-tuning, or RAG patterns.

Culture & Benefits

• High level of ownership and autonomy in a small, growing team.
• Opportunity to shape the ML stack and infrastructure from the ground up.
• Exposure to the full ML lifecycle rather than siloed responsibilities.
• Collaborative environment with direct access to stakeholders and cross-functional teams.
• Impactful work serving millions of survey respondents globally.