AI Engineer

TL;DR

AI Engineer: 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 deploying models for production, monitoring performance, and ensuring reproducibility.

Location: Hybrid, with options for fully remote work or from an office in hirify.global's global network.

Company

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

What you will do

• Design, train, and evaluate machine learning models (supervised, unsupervised, NLP, etc.).
• Build scalable data and ML pipelines using modern tools.
• Collaborate with subject matter experts and analysts to prepare training datasets.
• Deploy models for production (batch or real-time inference).
• Monitor and maintain model performance and data quality.
• Optimize models for performance, interpretability, and cost.

Requirements

• 3+ years of experience as a Machine Learning Engineer or in a similar role.
• Proficiency in Python, including hands-on experience with scikit-learn, pandas, NumPy, and matplotlib.
• Strong understanding of core ML concepts (regression, classification, clustering, model validation, 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.
• English: Upper-Intermediate (B2) and above required.

Nice to have

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

Culture & Benefits

• Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and other industries.
• Opportunity for professional, financial, and career growth, with mentoring and adaptation systems for new employees.
• Access to a corporate training portal and English courses.
• Compensation for professional certifications (AWS, PMP, etc.).
• Private health insurance and compensation for sports activities.
• Referral program.
• Bright corporate life including parties, pizza days, PlayStation, fruits, coffee, snacks, and movies.