Machine Learning Engineer

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October 31

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Quantiphi

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Artificial Intelligence • Enterprise • Education

Quantiphi is a leading AI-first digital engineering company that leverages a decade of industry expertise to empower businesses through scalable, secure, and adaptable AI solutions. By integrating cutting-edge technology with real-world applications, Quantiphi transforms organizations across various sectors including healthcare, finance, education, and retail. Their services span AI applications, data analytics, cloud infrastructure modernization, and custom AI implementations. Quantiphi partners with technology giants like AWS, Google Cloud, NVIDIA, and others to drive AI adoption and deliver transformational opportunities for enterprises.

1001 - 5000 employees

Founded 2013

🤖 Artificial Intelligence

🏢 Enterprise

📚 Education

💰 Series A on 2019-12

📋 Description

• Design, develop, train, and fine-tune machine learning models, including custom and pre-trained models on Google Cloud Vertex AI and Document AI. • Build and manage custom Document AI processors such as Custom Document Splitter, Custom Document Classifier, and Custom Document Extractor. • Work with pre-trained Document AI processors and customize them for business-specific document understanding tasks. • Develop and deploy ML solutions using GCP services like Cloud Functions, Cloud Run, Firestore, Cloud SQL, Cloud Storage, and BigQuery. • Design and implement data preprocessing pipelines for large-scale, unstructured, and semi-structured data. • Integrate ML models into production systems via secure and scalable APIs. • Evaluate model performance using standard ML metrics, perform model validation, and optimize for accuracy, latency, and efficiency. • Collaborate with cross-functional teams (Data Engineers, Software Developers, and Product Teams) to ensure seamless model integration and delivery. • Troubleshoot and debug ML pipelines, training jobs, and model deployment issues. • Maintain proper version control of code, models, and configurations using Git/GitHub. • Follow best practices for ML lifecycle management, testing, and documentation.

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience. • Proven experience with Google Cloud Document AI (Custom Workbench: Splitter, Classifier, Extractor, and pre-trained processors). • Hands-on experience with Google Cloud Vertex AI for model training, tuning, and deployment. • Strong understanding and practical experience with Large Language Models (LLMs) and their fine-tuning. • Proficiency in Python and ML libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn). • Experience with ML model design, training, testing, evaluation, and fine-tuning. • Solid experience in data preprocessing and feature engineering. • Familiarity with GCP services such as Cloud Functions, Cloud Run, Firestore, Cloud Storage, Cloud SQL, and BigQuery. • Strong understanding of API integration for ML model deployment. • Proficiency in troubleshooting and debugging ML-related issues. • Experience with Git/GitHub for version control and collaboration.

🏖️ Benefits

• Be part of the fastest-growing AI-first digital transformation and engineering company in the world • Be a leader of an energetic team of highly dynamic and talented individuals • Exposure to working with fortune 500 companies and innovative market disruptors • Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud