Job Description
Are you ready to define the technological landscape of 2026?
Aether Dynamics is seeking a visionary Senior AI Engineer to join our elite engineering team. As we prepare to launch our most ambitious AI infrastructure projects yet, we need a leader who can bridge the gap between theoretical machine learning and scalable production systems. This is a rare opportunity to influence the roadmap that will define our industry for years to come.
At Aether Dynamics, we don't just predict the future; we build it. Our mission is to integrate advanced neural networks into core business operations, ensuring our clients stay ahead in a rapidly evolving digital ecosystem. If you are passionate about Generative AI, Large Language Models (LLMs), and ethical AI development, we want to meet you.
Responsibilities
- Architect and deploy scalable machine learning models designed for high-volume, real-time processing in the 2026 era.
- Lead the full lifecycle of AI development, from data ingestion and feature engineering to model training and MLOps deployment.
- Collaborate with cross-functional product teams to translate complex business requirements into robust AI solutions.
- Optimize existing algorithms to improve latency, accuracy, and cost-efficiency.
- Establish best practices for model governance, ensuring compliance with data privacy regulations and ethical AI standards.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and innovation.
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, or a related technical field (or equivalent practical experience).
- Minimum of 5+ years of professional experience in machine learning engineering or data science.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Strong understanding of distributed systems and cloud platforms (AWS, GCP, or Azure).
- Proven experience with MLOps tools, containerization (Docker/Kubernetes), and CI/CD pipelines.
- Demonstrated track record of deploying successful AI products to production environments.