ML Engineer (AI)

TL;DR

ML Engineer (AI): Designing, training, and deploying scalable machine learning models and building ML pipelines with an accent on production-ready AI solutions and model optimization. Focus on integrating foundation/generative models (LLMs) and MLOps practices for performance, interpretability, and reproducibility.

Location: Remote, Hybrid, or Onsite. Must be based in one of the following European countries: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Czech Republic, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Kosovo, Latvia, Lithuania, Luxembourg, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, The Netherlands.

Company

hirify.global is a pre-IPO software development company that provides full-cycle services to enterprises and mid-sized firms worldwide.

What you will do

• Design, train, and evaluate machine learning models (supervised, unsupervised, NLP).
• Build scalable data and ML pipelines using modern tools.
• Deploy models for production (batch or real-time inference).
• Monitor and maintain model performance and data quality.
• Optimize models for performance, interpretability, and cost.
• Document ML workflows and ensure reproducibility.

Requirements

• 2+ years of experience as a Machine Learning Engineer.
• Proficiency in Python with libraries like scikit-learn, pandas, NumPy, and matplotlib.
• Strong understanding of core ML concepts – regression, classification, clustering, model validation, and performance metrics.
• Practical experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Proven experience building, training, and deploying ML models using AWS SageMaker.
• Familiarity with AWS Bedrock for working with foundation and generative models (e.g., fine-tuning and orchestration of LLMs).
• Hands-on experience with data preprocessing, feature engineering, and model evaluation.
• Knowledge of SQL and experience working with structured and semi-structured datasets.
• Understanding of ML model deployment (e.g., REST APIs with FastAPI or Flask; model packaging and containerization with Docker).
• Exposure to MLOps practices – pipeline automation, model versioning, monitoring, and reproducibility.
• Familiarity with version control systems (e.g., Git).
• Strong analytical thinking, communication, and problem-solving skills.
• Willingness to stay current with emerging ML techniques, frameworks, and cloud AI tools.
• English: B2 (Upper-Intermediate) and above required.
• German: B1 (Intermediate) and above required.

Culture & Benefits

• Opportunity to work with leaders in FinTech, Healthcare, Retail, and Telecom.
• Flexible work options: fully remote, office, or hybrid.
• Professional, financial, and career growth with mentoring and adaptation systems.
• Access to a corporate training portal and certification compensation (AWS, PMP, etc).
• Private health insurance and compensation for sports activities.
• English courses and referral program.