Teams at Qutwo

Design, deploy, and maintain production-grade LLM architectures and autonomous agentic AI systems. Collaborate with scientists, engineers, and customers to solve complex problems, lead cross-functional technical initiatives, translate business goals into roadmaps, and promote reliable, observable, safe, high-quality engineering. Mentor teams, conduct architecture and code reviews, and advance systems involving RAG, fine-tuning, agent memory, evaluation, vector databases, distributed computing, GPU optimization, and quantum computing.

Productize machine learning pipelines for deep neural network compression and build secure, observable SaaS products. Responsibilities span ML training and inference workflows, backend services, frontend development, cloud infrastructure, containers, Kubernetes, CI/CD, observability, and security. The role owns architecture, deployment, operations, cost efficiency, and technical trade-offs involving model size, latency, memory, and accuracy. Candidates need broad production engineering experience, practical PyTorch or TensorFlow expertise, security awareness, and strong cross-functional communication skills.

Develop, implement, and benchmark classical and quantum optimization solutions for enterprise problems. Translate business problems into mathematical models, design QUBO/quantum approaches, use major quantum frameworks, and communicate results to customers and interdisciplinary teams.