Job Description
Are you ready to engineer the intelligence of tomorrow? Quantum Leap Innovations is on a mission to define the technological landscape of 2026. We are seeking a visionary Lead AI Engineer to spearhead our next-generation generative AI initiatives. You will be at the forefront of innovation, building scalable models that will power the next decade of human-machine interaction.
In this pivotal role, you won't just be maintaining systems; you will be architecting the future. You will collaborate with a world-class team of researchers, engineers, and product strategists to solve complex problems in natural language processing (NLP) and computer vision. If you are passionate about pushing the boundaries of what is possible with artificial intelligence, we want to hear from you.
Responsibilities
- Architect Next-Gen Systems: Design and implement robust, scalable AI architectures capable of handling high-volume, real-time data processing.
- Lead Research & Development: Drive the research roadmap for 2026, exploring emerging paradigms in large language models (LLMs) and autonomous agents.
- Mentorship & Culture: Cultivate a high-performance engineering culture by mentoring junior developers and conducting technical workshops.
- Optimization: Continuously optimize model inference latency and reduce operational costs through efficient training strategies.
- Product Integration: Partner with product teams to translate complex AI capabilities into intuitive, user-facing features.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years in a lead or senior architecture role.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with distributed training frameworks (Ray, Horovod).
- Domain Expertise: Strong background in NLP, Transformers, or Computer Vision.
- Cloud Mastery: Proven experience deploying models on AWS, Azure, or GCP using containerization (Docker, Kubernetes).