Job Description
Are you ready to define the future of artificial intelligence? NeuralNext Systems is seeking a visionary Senior Generative AI Engineer to join our elite R&D team. We are pushing the boundaries of what's possible with Large Language Models (LLMs) and Autonomous Agents. In this role, you will architect the next generation of intelligent software solutions that power enterprise applications globally.
We offer a competitive compensation package, equity opportunities, and the chance to work with a world-class team of researchers and engineers. If you are passionate about AI ethics, model optimization, and building scalable systems, we want to hear from you.
We offer a competitive compensation package, equity opportunities, and the chance to work with a world-class team of researchers and engineers. If you are passionate about AI ethics, model optimization, and building scalable systems, we want to hear from you.
Responsibilities
- Design and implement scalable machine learning pipelines for LLM fine-tuning and deployment.
- Optimize model inference latency and cost-efficiency for real-time, high-volume applications.
- Collaborate with cross-functional teams to integrate advanced AI capabilities into core product features.
- Research and prototype novel architectures in Natural Language Processing (NLP) and Computer Vision.
- Ensure the ethical and responsible use of AI models through rigorous testing, bias mitigation, and explainability studies.
- Guide junior engineers and contribute to the technical roadmap for our AI research initiatives.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent professional experience).
- 5+ years of professional experience in Machine Learning or AI engineering.
- Deep proficiency in Python, PyTorch, and TensorFlow.
- Experience with Hugging Face Transformers, LangChain, and vector databases (Pinecone, Weaviate).
- Strong understanding of distributed systems and cloud infrastructure (AWS/GCP/Azure).
- Experience with MLOps tools (Docker, Kubernetes, MLflow, Airflow).