Job Description
Are you ready to define the technological landscape of 2026 and beyond? QuantumLeap Systems is seeking a visionary Senior AI Architect to lead our next-generation generative intelligence initiatives. We are building the infrastructure that will power the autonomous enterprises of tomorrow.
In this role, you won't just maintain systems; you will architect the future. You will work at the intersection of cutting-edge research and scalable engineering, pushing the boundaries of Large Language Models (LLMs), Computer Vision, and Autonomous Agents.
If you are a problem solver who thrives in ambiguity and wants to build the tools that define the next era of human-computer interaction, we want to hear from you.
Responsibilities
- Architect & Deploy: Design and implement high-scale, low-latency AI infrastructure capable of processing billions of tokens daily.
- Model Optimization: Fine-tune and optimize proprietary LLMs for specific industry verticals, focusing on inference speed and cost-efficiency.
- MLOps Leadership: Establish and maintain robust MLOps pipelines, ensuring reproducibility, version control, and automated deployment workflows.
- Research & Innovation: Stay ahead of the curve on emerging AI trends (e.g., Multimodal AI, Agentic workflows) and prototype novel solutions.
- Team Mentorship: Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Collaboration: Partner with product managers and engineering teams to translate complex AI capabilities into user-centric features.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related technical field (PhD preferred).
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years in a lead or architect role.
- Technical Stack: Deep expertise in PyTorch, TensorFlow, or JAX; extensive experience with Hugging Face, LangChain, or similar frameworks.
- Infrastructure: Proficiency in cloud platforms (AWS/GCP/Azure) and containerization technologies (Docker, Kubernetes).
- Programming: Advanced proficiency in Python; familiarity with C++ or Rust for performance-critical components.
- Soft Skills: Exceptional communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.