Job Description
Are you ready to shape the future of intelligent systems?
Nebula AI Labs is at the forefront of generative AI and predictive analytics. We are seeking a visionary Senior AI Engineer to join our elite team and deploy state-of-the-art models that impact millions of users worldwide. If you are passionate about pushing the boundaries of what is possible with Large Language Models (LLMs) and Computer Vision, we want to hear from you.
In this role, you will not just write code; you will architect the brain of our next-generation SaaS platform. You will work in a fast-paced, collaborative environment with top-tier talent, leveraging cutting-edge infrastructure to solve complex problems.
Responsibilities
- Model Development: Design, train, and fine-tune advanced machine learning models, including LLMs and transformers, to solve real-world business problems.
- System Architecture: Build scalable and robust MLOps pipelines to ensure seamless model deployment, monitoring, and retraining workflows.
- Performance Optimization: Optimize model inference latency and resource efficiency to ensure high availability in production environments.
- Innovation: Research and prototype new algorithms and techniques to stay ahead of industry trends in AI.
- Cross-Functional Collaboration: Partner with product managers and data scientists to define AI product requirements and translate them into technical solutions.
- Code Quality: Write clean, maintainable, and well-documented code, adhering to industry best practices and agile methodologies.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 5+ years of professional experience in AI/ML engineering, with a strong portfolio of deployed models.
- Programming: Expert proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX).
- LLM Knowledge: Hands-on experience with fine-tuning LLMs (e.g., GPT, BERT, Llama) and RAG (Retrieval-Augmented Generation) architectures.
- Cloud & MLOps: Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (MLflow, Kubeflow, Docker, Kubernetes).
- Problem Solving: Strong analytical skills with the ability to debug complex issues and optimize system performance under pressure.