ML Engineer (AI)

TL;DR

ML Engineer (AI): Designing, training, and evaluating machine learning models and building scalable data and ML pipelines with an accent on model performance, interpretability, and cost. Focus on fine-tuning and orchestration of LLMs, implementing MLOps practices, and ensuring reproducibility.

Location: Remote, hybrid, or onsite (globally distributed)

Company

hirify.global is a global software development and IT services company with over 18 years of experience, specializing in digital transformation for enterprises and mid-sized businesses.

What you will do

• Design, train, and evaluate various machine learning models (supervised, unsupervised, NLP).
• Build and maintain scalable data and ML pipelines.
• Collaborate on preparing accurate training datasets.
• Deploy models for production using batch or real-time inference.
• Monitor and optimize model performance, interpretability, and cost.
• Document ML workflows to ensure reproducibility.

Requirements

• Experience as a Machine Learning Engineer or similar role for 3+ years.
• Proficiency in Python, including libraries like scikit-learn, pandas, NumPy, and matplotlib.
• Strong understanding of core ML concepts: regression, classification, clustering, model validation.
• Practical experience with deep learning frameworks (TensorFlow, PyTorch, or Keras).
• Proven experience building, training, and deploying ML models using AWS SageMaker.
• Familiarity with AWS Bedrock for foundation and generative models (e.g., LLMs).
• Hands-on experience with data preprocessing, feature engineering, model evaluation, and SQL.
• Understanding of ML model deployment (e.g., REST APIs with FastAPI or Flask, Docker).
• Exposure to MLOps practices (pipeline automation, model versioning, monitoring).
• English: B2+ required.

Nice to have

• Experience with other cloud platforms (GCP, Azure) and managed ML services.
• Experience with MLFlow, DVC, or Airflow.
• Familiarity with CI/CD for ML systems and big data tools (Spark, Hadoop).
• Knowledge of data security and ethical AI considerations.
• Experience with natural language processing (NLP), computer vision, or agentic AI.

Culture & Benefits

• Opportunity to work with leaders in FinTech, Healthcare, Retail, and Telecom.
• Flexible work options: fully remote, hybrid, or onsite.
• Guaranteed professional, financial, and career growth with mentoring and adaptation systems.
• Access to a comprehensive corporate training portal.
• Compensation for certifications (e.g., AWS, PMP).
• Private health insurance and compensation for sports activities.
• English courses and a referral program.