Job Description
Are you ready to architect the future of technology? Nebula Core Technologies is seeking a visionary Lead AI Architect to define our roadmap for 2026 and beyond. In this pivotal role, you will bridge the gap between theoretical AI research and scalable production systems, driving the next generation of generative intelligence.
We are looking for a thought leader who thrives in ambiguity and possesses the technical prowess to build systems that not only perform today but are adaptable for the transformative technologies of tomorrow. If you are passionate about pushing the boundaries of what's possible with Large Language Models (LLMs) and autonomous agents, we want to hear from you.
Why Nebula Core?
- Work on cutting-edge projects that define the next decade of tech.
- Competitive compensation package with equity options.
- Flexible remote-first culture with state-of-the-art equipment.
Responsibilities
- Architect Design: Design and deploy robust, scalable AI infrastructure capable of handling petabyte-scale data processing for 2026 workloads.
- Model Strategy: Lead the research and integration of next-gen generative AI models, optimizing for latency, accuracy, and cost-efficiency.
- Team Leadership: Mentor a high-performing team of data scientists and ML engineers, fostering a culture of innovation and continuous learning.
- System Integration: Oversee the seamless integration of AI solutions into existing software ecosystems and cloud environments.
- Future-Proofing: Anticipate industry trends and emerging technologies to ensure our architecture remains relevant and future-proof.
- Performance Optimization: Continuously monitor, tune, and improve model performance in production environments.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of experience in AI/ML engineering, with at least 2 years in a senior or leadership capacity.
- Technical Skills: Deep expertise in Python, PyTorch, TensorFlow, and distributed computing frameworks.
- Domain Knowledge: Strong understanding of Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Cloud Mastery: Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Communication: Excellent ability to translate complex technical concepts for diverse stakeholders.