ML Engineer

Quantum HR is a premier human resources consulting firm dedicated to connecting exceptional talent with leading organizations worldwide. We specialize in providing bespoke recruitment solutions, leveraging deep industry insights and a global network to help companies build high-performing teams by matching them with professionals who truly fit their needs and culture.

About the Role

We are hiring a Machine Learning Engineer to design, build, and deploy advanced ML models powering Digified’s digital identity verification and contracting platforms.

You will work on real-world, high-impact problems in **computer vision, biometrics, OCR, fraud detection, and NLP **, delivering production-grade ML systems integrated into APIs and enterprise workflows.

This is a hands-on engineering role focused on taking models from research to scalable, secure, and compliant production environments.

Key Responsibilities

• Develop and optimize ML models for face recognition, liveness detection, OCR, fraud detection, and NLP-based document understanding
• Build and maintain production-grade ML pipelines and APIs for real-time inference
• Deploy and scale models using MLOps best practices (CI/CD, versioning, monitoring, retraining)
• Optimize model performance for latency, accuracy, and reliability in production environments
• Design and manage high-quality datasets with proper annotation and data governance
• Monitor model performance (drift, accuracy, false acceptance/rejection rates)
• Collaborate with backend, mobile, DevOps, and product teams to integrate ML systems
• Ensure secure, compliant ML practices aligned with regulatory and data privacy standards
• Conduct research and propose improvements in model architectures and system performance

Requirements

• 3–7 years of experience in Machine Learning, Deep Learning, or Computer Vision
• Strong Python programming skills
• Experience with PyTorch and/or TensorFlow/Keras
• Strong background in at least one of: Computer Vision, NLP, or Fraud Detection
• Experience with ML deployment (FastAPI, Flask, Docker, Kubernetes)
• Understanding of MLOps principles (model versioning, monitoring, retraining pipelines)
• Solid foundation in mathematics (linear algebra, probability, statistics)
• Experience working with production ML systems

Nice to Have

• Experience with face recognition, OCR, or liveness detection systems
• Exposure to Vision Transformers, Siamese Networks, CRNNs, or LLM-based systems
• Experience with distributed training or GPU acceleration
• Familiarity with MLflow or experiment tracking tools
• Knowledge of identity standards (FIDO, NIST 800-63-3)
• Background in fintech, regtech, or anti-fraud systems
• Experience with trust & safety or government-grade systems

Benefits

• Work on cutting-edge ML systems in digital identity and fraud prevention
• High-impact role with production ownership from research to deployment
• Exposure to enterprise-grade AI systems used in regulated environments
• Strong engineering culture with cross-functional collaboration
• Opportunity to work on advanced CV/NLP/biometric systems at scale
• Career growth in a fast-scaling AI-driven product company