ML Engineer

🕒 May 12

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ITRex Group

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2009

🤖 Artificial Intelligence

🏢 Enterprise

🔧 Hardware

Artificial Intelligence • Enterprise • Hardware

ITRex Group is a technology consultancy and engineering firm that builds applied AI, generative AI, data platforms, and intelligent edge/IoT solutions for enterprise clients across healthcare, logistics, manufacturing, and other regulated industries. They provide end-to-end services including AI strategy and readiness assessments, product discovery and PoCs, LLM fine-tuning, MLOps/LLMOps and governance, data architecture and platform modernization (warehouses, lakes, vector DBs), and embedded hardware and edge software development. ITRex focuses on production-ready, compliant deployments that integrate models, data infrastructure, and edge devices to deliver scalable, secure AI-driven products and operations.

📋 Description

• Design, develop, and deploy machine learning models for predictive analytics, classification, NLP, and other data-driven tasks • Implement data pipelines for ingestion, preprocessing, feature engineering, and model training • Containerize ML models and applications using Docker for scalable and reproducible deployments • Deploy and maintain ML solutions in cloud environments (AWS/Snowflake) • Optimize model performance, latency, and resource utilization for real-time or batch inference • Monitor and troubleshoot ML models in production, ensuring reliability and robustness • Сollaborate with Product, Engineering, Data, and business stakeholders to define project requirements and integrate ML models into production systems • Conduct rigorous model evaluation using appropriate metrics to ensure performance and fairness • Assess whether machine learning is necessary for a given problem or if alternative rule-based/statistical approaches are more appropriate

🎯 Requirements

• 4+ years of experience as a Software Engineer, with at least 3 years in an ML Engineer role • Strong understanding of machine learning techniques, including supervised & unsupervised learning, NLP, deep learning fundamentals, and model evaluation • Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy • Hands-on experience in containerizing ML applications using Docker for scalable deployment • Practical experience with at least one cloud provider (AWS, GCP) • Strong background in working with large datasets, SQL/NoSQL databases • Ability to decompose complex problems into well-structured ML tasks • Skilled at assessing whether ML is the best approach or if a simpler solution (e.g., heuristic rules, statistical methods) would be more effective • Expertise in debugging, optimizing, and enhancing models for performance, efficiency, and interpretability • Experience maintaining ML workflows to ensure reproducibility, scalability, and operational efficiency • Excellent communication skills, capable of explaining ML concepts to both technical peers and non-technical stakeholders • Collaborative, product-focused approach within Agile, cross-functional environments • Proactive mindset with a strong sense of ownership with the ability to lead ML tasks end-to-end, from discovery and experimentation to production deployment and support • Continuous learning mindset with awareness of current ML/AI trends, tools, and best practices • English proficiency at an Upper-Intermediate level or above

🏖️ Benefits

• Remote flexibility: Work where and how you work best - we trust you to deliver • Fair compensation: Competitive salary + benefits that matter (medical, learning) • Ownership opportunities: See a problem worth solving? Own it. We back smart risks over bureaucratic safety • AI enhancement: We leverage AI to make you faster and stronger - complementing your abilities, not replacing them • Learning investment: English classes, professional development • Career progression: Real paths up, not just sideways shuffling • Responsive teammates: No ignored Slacks, no "not my problem" attitudes • Supportive culture: When you're stuck, people help. When things break, we fix them together • Human connections: Regular meetups, tech talks, and actual relationships beyond work