Job Description
We are building the operating system for the next generation of intelligent machines. As a Senior AI Architect at Nexus Future Labs, you won't just be writing code; you will be defining the architectural paradigms for 2026 and beyond. We are seeking a visionary engineer to lead our R&D efforts in Large Language Models (LLMs), autonomous agents, and multimodal generative systems.
The Role:
You will be at the forefront of the AI revolution, bridging the gap between theoretical research and production-grade deployment. Our mission is to create AI systems that are not only intelligent but also ethical, scalable, and deeply integrated into the fabric of enterprise workflows.
Responsibilities
- Architect and Design: Design scalable, fault-tolerant infrastructure for training and deploying state-of-the-art Generative AI models.
- R&D Leadership: Lead research initiatives into emerging AI architectures, including Retrieval-Augmented Generation (RAG) and AutoML frameworks.
- Pipeline Optimization: Oversee the full machine learning lifecycle, from data ingestion and preprocessing to model fine-tuning and inference optimization.
- Model Deployment: Implement production-grade APIs and microservices to integrate generative AI capabilities into client-facing applications.
- Technical Mentorship: Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Ethical AI: Ensure all deployed models adhere to safety guidelines, reduce bias, and maintain transparency.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, Mathematics, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering with a strong focus on AI/ML.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Deep understanding of Transformer architectures and Attention mechanisms.
- Generative AI: Proven experience working with LLMs (e.g., GPT, LLaMA, Claude), fine-tuning techniques, and prompt engineering.
- Infrastructure: Experience with cloud platforms (AWS/GCP/Azure) and containerization tools (Docker, Kubernetes).
- Problem Solving: Ability to tackle complex, ambiguous problems and deliver robust engineering solutions under tight deadlines.