Senior Machine Learning Engineer (Computer Vision)

We are looking for a highly skilled Senior Machine Learning Engineer with strong expertise in computer vision, NLP, and end-to-end machine learning model development. This is a hands-on individual contributor role focused on building intelligent AI solutions for identity verification, document processing, fraud detection, and regulatory technology use cases.

The ideal candidate should have a solid foundation in traditional machine learning and deep learning, with hands-on experience training models from scratch, conducting research, and deploying production-grade ML solutions. We are specifically looking for candidates who have worked on ML before the GenAI boom (2022 or earlier) and possess strong core ML fundamentals beyond LLM fine-tuning.

Responsibilities:

• Design, develop, train, and optimize machine learning and deep learning models for computer vision and NLP use cases.
• Build AI solutions for document verification, OCR, face matching, liveness detection, document classification, and fraud detection.
• Develop and improve NLP pipelines for text extraction, entity recognition, document understanding, and classification.
• Perform data preprocessing, feature engineering, model training, evaluation, and performance optimization.
• Deploy machine learning models into production environments using scalable APIs and cloud infrastructure.
• Build and maintain end-to-end ML pipelines, including training, inference, monitoring, and model versioning.
• Work closely with product, engineering, and business teams to translate real-world business problems into AI-driven solutions.
• Conduct experiments, benchmark models, and continuously improve model accuracy and efficiency.
• Document methodologies, experiments, and deployment workflows to support future scalability.
• Preferred Problem Areas: KYC / KYB automation, identity verification, document intelligence, OCR and intelligent document processing, face matching and liveness detection, fraud detection and anomaly detection, risk and compliance automation, and contract and document classification.

Requirements:

• A hands-on machine learning engineer who combines strong research fundamentals with real-world production experience.
• The ideal candidate has built and deployed computer vision and NLP solutions for identity verification, document intelligence, or fraud detection use cases and possesses deep expertise in model training, optimization, and scalable deployment.
• 4+ years of hands-on experience in machine learning or AI.
• Strong computer vision experience, including OCR, document classification, face recognition, or liveness detection.
• Strong NLP fundamentals with experience in text extraction, entity recognition, classification, or document understanding.
• Hands-on experience training machine learning and deep learning models from scratch.
• Strong understanding of supervised learning, feature engineering, optimization techniques, and model evaluation.
• Experience deploying ML models into production environments.
• Strong Python programming skills.
• Experience with TensorFlow, PyTorch, Scikit-learn, OpenCV, or similar frameworks.
• Experience building REST APIs for ML inference.
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure.
• Strong understanding of production monitoring, model performance tracking, and scalability.

Preferred Experience:

• Experience in KYC, KYB, Identity Verification, RegTech, FinTech, Fraud Detection, or Document AI domains.
• Experience with companies such as ZOOP.one, HyperVerge, or similar AI, computer vision, and identity verification platforms.
• Exposure to MLOps tools such as MLflow, Kubeflow, Docker, Kubernetes, or CI/CD pipelines.
• Experience reading research papers, conducting experiments, and improving model architectures.
• Publications or research contributions in AI, computer vision, or NLP are a plus.