Job Description
Are you ready to define the future of artificial intelligence? Nexus AI Systems is seeking a visionary Senior AI Architect to lead the development of our next-generation generative models. As we look toward 2026, we are building the infrastructure that will power the next decade of digital interaction. You will work at the intersection of cutting-edge research and scalable engineering, transforming theoretical AI concepts into production-ready applications that impact millions.
In this role, you will collaborate with a world-class team of researchers, engineers, and product managers to pioneer solutions in Large Language Models (LLMs), autonomous agents, and multimodal AI systems. If you thrive in a fast-paced, innovative environment and are passionate about the ethical advancement of AI, we want to hear from you.
Responsibilities
- Architect & Deploy: Design, train, and deploy state-of-the-art Large Language Models (LLMs) and generative AI agents for enterprise-scale applications.
- Optimization: Fine-tune existing models (e.g., Llama, GPT) to improve performance, reduce latency, and optimize inference costs.
- Research Implementation: Translate the latest research papers into practical, scalable code within our production pipeline.
- Infrastructure: Build and maintain robust MLOps pipelines for data ingestion, model training, validation, and continuous deployment.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- AI Governance: Establish best practices for AI safety, fairness, and bias mitigation in model outputs.
Qualifications
- Education: Master’s degree in Computer Science, Artificial Intelligence, or a related field (PhD preferred).
- Experience: 5+ years of professional experience in machine learning, NLP, or deep learning.
- Technical Stack: Proficiency in Python, PyTorch, or TensorFlow. Deep understanding of Transformer architectures.
- Frameworks: Experience with Hugging Face, LangChain, or similar AI framework ecosystems.
- Cloud: Strong experience deploying models on cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Demonstrated ability to tackle complex mathematical and engineering challenges.