Job Description
Join Nebula Systems, a pioneering leader in artificial intelligence, as we architect the infrastructure for the year 2026. We are looking for a visionary 2026-Ready LLM Architect to design scalable, secure, and transformative Large Language Model ecosystems. If you thrive on solving complex problems and are passionate about the future of generative AI, this is your opportunity to shape the next decade of technology.
In this role, you will bridge the gap between theoretical AI advancements and practical, high-impact applications. You will lead the strategy for integrating multimodal capabilities, ensuring our systems are not just current, but future-proof for the rapid evolution of AI.
Responsibilities
- Architect Future-Proof LLM Infrastructure: Design and deploy robust large-scale machine learning systems capable of handling the computational demands of 2026 and beyond.
- Strategic Model Integration: Oversee the integration of next-generation multimodal AI models, ensuring seamless communication between text, vision, and audio data streams.
- Optimization & Efficiency: Lead initiatives to reduce inference costs and improve model latency through advanced quantization and distillation techniques.
- R&D Leadership: Collaborate with R&D teams to prototype novel AI architectures, focusing on ethical AI and responsible deployment.
- System Scalability: Ensure our AI platforms can scale horizontally to support millions of concurrent users without degradation in performance.
- Cross-Functional Collaboration: Work closely with product managers, data scientists, and security engineers to define roadmap priorities and technical standards.
Qualifications
- Advanced Degree: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Core Expertise: Deep experience in Python, PyTorch, TensorFlow, and modern MLOps tooling (Kubeflow, MLflow, Airflow).
- LLM Proficiency: Proven track record of working with Transformer architectures, RAG (Retrieval-Augmented Generation), and fine-tuning large language models.
- System Design: Strong understanding of distributed systems, microservices, and cloud-native architecture (AWS, GCP, or Azure).
- Problem Solving: Exceptional ability to troubleshoot complex system bottlenecks and optimize data pipelines.
- Communication: Excellent verbal and written communication skills, capable of explaining complex technical concepts to diverse stakeholders.