Overview

Senior Machine Learning Engineer (Computer Vision & NLP)

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.
• 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
• Contract and document classification

Qualifications

• Location: Pune (Onsite)
• About the Role:
• 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 in 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.
• 4+ years of hands-on experience in
• Strong
• Strong NLP fundamentals with experience in text extraction, entity recognition, classification, or document understanding.
• Hands-on experience training
• 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 and or GCP.
• 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.
• hands-on Machine Learning Engineer
• strong research fundamentals