Job Description
Welcome to Nebula AI Solutions, where we are defining the landscape of intelligent systems for the year 2026 and beyond. We are a fast-paced, forward-thinking team dedicated to developing next-generation Large Language Models (LLMs) and Generative AI solutions that redefine human-machine interaction.
We are looking for a visionary Senior AI Engineer to join our core research division. In this role, you will not just implement existing models; you will architect the infrastructure that powers the future of AI. If you are passionate about pushing the boundaries of what is possible in artificial intelligence and want to work on projects that will impact the world in 2026, we want to meet you.
Why Join Nebula AI?
- Future-Ready Tech Stack: Work with the latest in Python, PyTorch, and Quantum-inspired computing.
- Impactful Work: Your code will be used by millions, optimizing efficiency and creativity.
- Elite Team: Collaborate with Ph.D. researchers and industry veterans from top tech firms.
- Competitive Package: We believe in rewarding top-tier talent with industry-leading compensation.
Responsibilities
- Architect Advanced AI Models: Design, train, and deploy scalable machine learning models, specifically focusing on Generative AI and LLMs for the 2026 market.
- Optimization & Performance: Continuously monitor, evaluate, and improve model accuracy, latency, and efficiency using distributed computing frameworks.
- Research & Development: Stay ahead of the curve by researching cutting-edge algorithms and integrating them into our production pipeline.
- Collaboration: Partner with product managers, data scientists, and software engineers to translate technical requirements into robust engineering solutions.
- Code Review & Mentorship: Conduct rigorous code reviews and mentor junior engineers to maintain high standards of technical excellence.
Qualifications
- Education: Master’s or Ph.D. degree in Computer Science, Mathematics, Statistics, or a related field with a focus on Artificial Intelligence.
- Experience: Minimum of 5+ years of professional experience in machine learning engineering or data science.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with MLOps tools (e.g., Kubeflow, MLflow).
- Deep Learning: Strong understanding of neural networks, NLP, and transformer architectures.
- Problem Solving: Proven track record of solving complex, ambiguous problems with innovative technical solutions.