We are seeking an innovative and experienced Machine Learning Engineer at Architect level with a strong foundation in both traditional data science and modern Generative AI. The ideal candidate will lead the design, development, and deployment of high-impact, data-driven solutions on our Azure cloud infrastructure. You will be responsible for architecting complex systems, including multi-agent platforms and computer vision solutions, optimizing legacy models, and providing technical leadership to cross-functional teams to solve challenging business problems.

Must-Have Skills & Experience:

- Proven experience architecting, developing, and deploying traditional and deep learning solutions at scale, from concept to production.
- Lead end-to-end ML lifecycle including data preparation, feature engineering, model development, validation, deployment, and monitoring.
- Provide technical leadership, mentorship, and architecture-level guidance to project teams.
- Demonstrated expertise in designing and implementing complex multi-agent systems.
- Experience with agentic design patterns such as supervisor-worker and orchestrator-led group chats to automate intricate business processes (e.g., invoice processing, document automation).
- Experience with data augmentation techniques and human-in-the-loop annotation processes for large-scale model training.
- Evaluate and optimize existing models using traditional ML techniques. Proven expertise in traditional ML algorithms (regression, decision trees, SVM, ensemble models, clustering, Random Forest, XGBoost).




- Deep understanding of ML pipeline orchestration and model lifecycle management with production-grade implementation experience.
- Ensure adherence to MLOps best practices and drive implementation on Azure cloud.
- Extensive experience in Azure cloud services including Azure Machine Learning, Azure Data Factory, Blob Storage, Azure DevOps, and Azure Container Apps.
- Leveraged Azure Cognitive Search and Azure OpenAI Service to build scalable and efficient knowledge retrieval systems, enabling real-time semantic search and contextual answer generation.
- Designed and implemented RAG pipelines on Microsoft Azure, integrating large language models (LLMs) with domain-specific knowledge bases to enhance AI-driven information retrieval and response accuracy.
- Experience with evaluation, monitoring and observability frameworks for Agentic workflows.
- Experience designing fault-tolerant systems with robust error handling, fallback mechanisms, and state management for complex, multi-step AI workflows.
- Ability to design and review ML architecture and system integration strategies with hands-on experience in production deployments.
- Certifications in Azure AI Engineer or Azure Solutions Architect.
- Excellent problem-solving, communication, and stakeholder management skills with experience presenting technical solutions to business stakeholders.

Positive-to-Have Skills:

- Collaboration skills with data scientists, data engineers, and product stakeholders to convert business requirements into scalable ML models.
- Contributions to open-source projects.

Skills:- Windows Azure, Machine Learning (ML), Generative AI (GenAI), Azure OpenAI and Retrieval Augmented Generation (RAG)

📌 Architect ML Engineer (Azure) (Bengaluru)
🏢 Quantiphi
📍 Bengaluru