Job Description
Join the Future of Intelligence at Nebula Dynamics
We are seeking a visionary Senior AI Engineer to lead the development of next-generation artificial intelligence systems. At Nebula Dynamics, we are pushing the boundaries of Large Language Models (LLMs), Computer Vision, and Reinforcement Learning to solve complex problems in autonomous systems and enterprise automation. If you are passionate about building scalable, ethical, and high-performance AI solutions, we want to hear from you.
In this role, you will architect and deploy cutting-edge machine learning pipelines, collaborate with world-class researchers, and directly impact products used by millions. You will have the opportunity to work in a hybrid environment, driving innovation from model training to production deployment.
Responsibilities
- Design & Development: Architect and implement scalable machine learning models and deep learning frameworks, focusing on NLP and predictive analytics.
- Model Optimization: Optimize existing models for speed, accuracy, and cost-efficiency using techniques like quantization, pruning, and distillation.
- Production Deployment: Deploy AI models into production environments using Kubernetes, Docker, and cloud-native services (AWS/GCP).
- Research & Innovation: Stay at the forefront of AI research, exploring new methodologies such as Transformers, GANs, or Federated Learning.
- Collaboration: Work closely with cross-functional teams including Data Scientists, Backend Engineers, and Product Managers to define technical requirements.
- Mentorship: Mentor junior engineers and conduct code reviews to maintain high engineering standards.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field; or equivalent practical experience.
- Experience: 5+ years of professional experience in machine learning or AI engineering.
- Programming: Expert-level proficiency in Python (PyTorch, TensorFlow, Scikit-learn).
- Infrastructure: Strong experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker, Kubernetes).
- Tools: Familiarity with MLOps tools (MLflow, Kubeflow, Airflow) and version control (Git).
- Communication: Excellent written and verbal communication skills with the ability to explain complex technical concepts to non-technical stakeholders.