Job Description
Are you ready to define the future of artificial intelligence? Quantum Dynamics Inc. is seeking a visionary Senior AI/ML Architect to lead our groundbreaking initiatives for the 2026 roadmap. We are building the next generation of autonomous agents and generative intelligence systems that will redefine industry standards.
In this role, you won't just implement existing models; you will architect the foundational infrastructure for the AI evolution. You will collaborate with cross-functional teams of data scientists, engineers, and product managers to deliver scalable, high-performance machine learning solutions that drive our competitive advantage.
Why Join Us?
- Work on cutting-edge Agentic AI and Large Language Model (LLM) infrastructures.
- Competitive compensation package and equity opportunities.
- Flexible remote-first culture with a focus on innovation.
If you are passionate about pushing the boundaries of what's possible in AI and want to leave a lasting impact on the technology of tomorrow, we want to hear from you.
Responsibilities
- Architect and design scalable, fault-tolerant AI infrastructure and MLOps pipelines for production environments.
- Lead the research and development of proprietary Generative AI models and agentic workflows.
- Optimize model performance, latency, and accuracy across diverse hardware architectures.
- Define technical vision and best practices for machine learning engineering within the organization.
- Mentor and guide junior data scientists and ML engineers to foster a culture of technical excellence.
- Collaborate with product stakeholders to translate complex business requirements into technical AI solutions.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related technical field (or equivalent practical experience).
- 5+ years of professional experience in Machine Learning Engineering or Data Science.
- Deep expertise in Python, PyTorch, TensorFlow, and modern MLOps tools (e.g., Kubeflow, MLflow, Airflow).
- Strong understanding of LLM architecture, fine-tuning techniques, and RAG (Retrieval-Augmented Generation).
- Proven track record of deploying AI models into production at scale with measurable business impact.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).