Job Description
About QuantumLeap Technologies: We are pioneering the infrastructure for the next decade of intelligent computing. As we move towards the 2026 technology roadmap, we are seeking a visionary Senior AI & Machine Learning Architect to lead the development of scalable, generative AI solutions that redefine industry standards.
Role Overview:
In this pivotal role, you will not just write code; you will architect the neural foundations of our future products. You will be responsible for designing state-of-the-art Large Language Model (LLM) pipelines, optimizing inference performance for edge devices, and ensuring our AI systems are robust, secure, and scalable. If you are passionate about the convergence of deep learning and real-world application, this is your opportunity to shape the future.
Responsibilities
- Architect Scalable AI Systems: Design and implement end-to-end machine learning pipelines that handle high-volume data ingestion and real-time inference processing.
- Research & Innovation: Lead research initiatives in Generative AI and Reinforcement Learning to stay ahead of the 2026 technology curve.
- Model Optimization: Fine-tune pre-trained models and apply quantization techniques to deploy efficient models on cloud and edge environments.
- Technical Leadership: Mentor a team of junior engineers and data scientists, conducting code reviews and establishing best practices for AI engineering.
- Collaboration: Partner with product managers and software engineers to translate complex AI capabilities into user-friendly features.
- Infrastructure Management: Oversee MLOps workflows, ensuring seamless CI/CD pipelines for model training and deployment.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 5+ years of professional experience in Machine Learning Engineering, with at least 2 years in a leadership or architect role.
- Technical Skills: Deep expertise in Python, PyTorch, and TensorFlow. Strong understanding of Transformer architectures (BERT, GPT).
- MLOps: Proficiency in deploying models using Kubernetes, Docker, and cloud platforms (AWS/GCP/Azure).
- Problem Solving: Demonstrated ability to solve complex engineering problems and optimize system performance.
- Communication: Excellent written and verbal communication skills, capable of presenting technical concepts to non-technical stakeholders.