Top Tech Jobs & Startup Jobs

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.

Lead and coordinate multi-quarter enterprise design partnerships, translating strategic customer ambitions into executable technical scope, driving deployments, customer success, expansion, and codifying playbooks across BD, R&D, product, and client teams.

Design and implement quantum algorithms, run and benchmark them on quantum hardware versus classical alternatives, collaborate with scientists and customers, and provide technical leadership and mentorship.

Design the core architecture of Qutwo OS to enable hardware-aware execution across multiple quantum backends, translate research into production-ready platform components, define cloud-native and MLOps integrations, and lead and mentor the platform engineering team.

Build and operate cloud and ML infrastructure: maintain CI/CD pipelines, IaC, Kubernetes/Docker environments, observability (Prometheus/Grafana/OpenTelemetry), and support ML model deployment and platform reliability for engineering and research teams.

Lead the research team defining algorithms, applications, and scientific foundations of Qutwo OS. Set strategy, conduct original research, publish, and convert research into production-grade implementations while building and mentoring the team.

Lead end-to-end product development of Qutwo OS, translating research into production-ready enterprise orchestration for hybrid quantum-classical compute. Prioritize roadmap, run iterative cycles, coordinate customer deployments, and deliver first paying customers.

Conduct applied and exploratory AI research; build end-to-end ML solutions with scientists, engineers, and customers; translate complex problems into mathematical models; mentor junior researchers and deliver high-impact, scalable ML implementations for quantum-era applications.