Job Description
Join the Future of Intelligence.
Quantum Leap AI is pioneering the next generation of generative models. We are seeking a visionary Senior AI Engineer to lead the development of scalable, high-performance machine learning systems. If you are passionate about pushing the boundaries of Large Language Models (LLMs) and transforming complex data into actionable intelligence, we want to hear from you.
Why Join Us?
- Impact at Scale: Your work will directly influence how millions interact with AI.
- Top-Tier Team: Collaborate with industry experts and thought leaders.
- Competitive Stack: Work with the latest in PyTorch, Rust, and Cloud Native infrastructure.
The Role:
As a Senior AI Engineer, you will own the full lifecycle of our ML infrastructure—from model architecture and training pipelines to deployment and real-time inference optimization. You will bridge the gap between cutting-edge research and production-grade reliability.
Responsibilities
- Architecture Design: Design and implement scalable AI/ML architectures capable of handling high-volume, low-latency requests.
- Model Optimization: Optimize existing models for speed and accuracy using techniques such as quantization, pruning, and distillation.
- Pipeline Management: Build and maintain robust CI/CD pipelines for model training and evaluation.
- Cross-Functional Collaboration: Partner with product managers and data scientists to define technical requirements and roadmaps.
- Mentorship: Guide junior engineers and contribute to the technical culture of excellence within the engineering team.
- R&D: Stay at the forefront of AI research and evaluate new technologies (e.g., Transformers, Diffusion Models) for integration.
Qualifications
- Experience: 5+ years of professional experience in software engineering with a focus on Machine Learning or Artificial Intelligence.
- Technical Skills: Deep expertise in Python, PyTorch, or TensorFlow. Experience with Rust or Go is a strong plus.
- Education: MS or PhD in Computer Science, Mathematics, or a related quantitative field.
- System Design: Strong understanding of distributed systems, cloud computing (AWS/GCP/Azure), and containerization (Docker/K8s).
- Problem Solving: Proven track record of solving complex engineering challenges and improving system performance metrics.
- Communication: Excellent verbal and written communication skills for technical documentation and stakeholder presentations.