Job Description
The Opportunity:
Join QuantumLeap Systems as our Senior AI Architect and help define the technological roadmap for 2026. We are at the forefront of Generative AI, Autonomous Systems, and predictive modeling. In this role, you will not just implement existing models but architect the next generation of intelligent infrastructure that will power the enterprise of tomorrow. This is a unique opportunity to lead a high-impact team in a fast-paced, innovative environment.
Why Join Us?
- Work with state-of-the-art AI/ML frameworks.
- Competitive equity and benefits package.
- Flexible remote-first hybrid work model.
- Direct access to executive leadership.
What You Will Do:
You will be responsible for the end-to-end lifecycle of our AI initiatives, ensuring scalability, security, and performance. Your vision will shape the products we release in the coming years.
Responsibilities
- Architect and design scalable, high-performance Machine Learning systems capable of handling petabyte-scale data.
- Lead the research and integration of cutting-edge AI trends, specifically focusing on Large Language Models (LLMs) and Generative Agents for the 2026 roadmap.
- Oversee the full ML lifecycle, from data ingestion and feature engineering to model training, evaluation, and deployment.
- Mentor a team of talented data scientists and engineers, fostering a culture of technical excellence and continuous learning.
- Collaborate with cross-functional product teams to translate business requirements into robust technical solutions.
- Optimize model inference speeds and reduce operational costs through efficient engineering practices.
Qualifications
- 5+ years of experience in software engineering, with at least 3 years specifically in AI/ML architecture.
- Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related technical field (or equivalent practical experience).
- Expert proficiency in Python, PyTorch, and TensorFlow.
- Deep understanding of Deep Learning, Natural Language Processing (NLP), and Computer Vision.
- Strong background in distributed systems, cloud computing (AWS, GCP, or Azure), and containerization (Docker/Kubernetes).
- Proven track record of deploying production-grade AI models at scale.