Job Description
We are on a mission to define the technological landscape of 2026 and beyond. Quantum Leap Technologies is seeking a visionary Senior AI Architect to lead our next-generation machine learning initiatives. You will be responsible for architecting robust, scalable AI systems that solve complex enterprise challenges.
In this pivotal role, you will bridge the gap between theoretical AI research and practical, high-impact engineering solutions. If you are passionate about the future of Generative AI, Large Language Models (LLMs), and predictive analytics, we want to hear from you.
Responsibilities
- Architect AI Solutions: Design and implement scalable machine learning pipelines and infrastructure for our core products.
- Model Development: Lead the research and development of proprietary AI models, focusing on performance, accuracy, and efficiency.
- Strategic Roadmap: Define the technical vision and roadmap for AI integration leading up to 2026.
- Mentorship: Guide a team of junior data scientists and engineers, fostering a culture of innovation and continuous learning.
- Optimization: Optimize existing models for real-time inference and reduce computational costs.
- Collaboration: Work closely with product managers and stakeholders to translate business requirements into technical specifications.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years in a leadership or architect role.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Deep understanding of deep learning architectures (CNNs, RNNs, Transformers).
- Cloud Expertise: Experience deploying models on cloud platforms such as AWS, GCP, or Azure using containerization tools (Docker, Kubernetes).
- MLOps: Strong understanding of MLOps practices, CI/CD, and model versioning.
- Communication: Excellent verbal and written communication skills, capable of explaining complex technical concepts to non-technical audiences.