Associate Architect - Machine Learning (Azure) at Quantiphi

About the Role

What you'll do

• Lead the design, development, and deployment of high-impact, data-driven solutions on Azure cloud infrastructure.
• Architecting, developing, and deploying traditional and deep learning solutions at scale
• Leading end-to-end ML lifecycle from data preparation through production monitoring
• Providing technical leadership and architecture-level guidance to cross-functional teams
• Designing complex multi-agent systems with agentic design patterns
• Implementing data augmentation and human-in-the-loop annotation processes
• Evaluating and optimizing models using regression, ensemble models, clustering, Random Forest, and XGBoost
• Designing RAG pipelines on Azure integrating LLMs with domain-specific knowledge bases
• Building scalable knowledge retrieval systems with real-time semantic search
• Designing ML architecture and system integration strategies for production deployments
• Ensuring adherence to MLOps best practices on Azure cloud services
• Presenting technical solutions to business stakeholders

What we're looking for

• 8–14 years of experience in machine learning engineering at architect level
• Proven expertise in traditional ML algorithms (regression, decision trees, SVM, ensemble models, clustering, Random Forest, XGBoost)
• Demonstrated expertise designing and implementing complex multi-agent systems
• Experience with agentic design patterns (supervisor-worker, orchestrator-led group chats)

Skills & Technologies

Required

Benefits & Perks

• Health Insurance
• Flexible Leave Policy
• Learning Budget
• EPF / NPS

Why This Role is Good for Experienced Professionals

• Architect end-to-end ML and GenAI systems on Azure—including agentic workflows and RAG pipelines—while mentoring teams at a leading AI-first digital engineering firm.
• Architect, develop, and deploy traditional and deep learning solutions at scale from concept to production
• Lead end-to-end ML lifecycle including data prep, feature engineering, model development, validation, deployment, and monitoring
• Provide technical leadership, mentorship, and architecture-level guidance to project teams
• Design and implement complex multi-agent systems with supervisor-worker and orchestrator-led patterns
• Automate intricate business processes such as invoice processing and document automation
• Apply data augmentation and human-in-the-loop annotation for large-scale model training
• Evaluate and optimize existing models using traditional ML techniques
• Leverage Azure Cognitive Search and Azure OpenAI Service for RAG and semantic search
• Design fault-tolerant agentic workflows with robust error handling and state management
• Drive MLOps best practices on Azure Machine Learning, Data Factory, DevOps, and Container Apps

About Quantiphi

Industry

Technology

Company Size

500+

Website

quantiphi.com