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Information Technology 🏢 Full Time ⭐️ Verified

Lead AI Architect: Generative Intelligence

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
11 Mei 2026
Deadline
11 Mei 2027

Job Description

We are pioneering the next generation of Artificial General Intelligence (AGI) and are seeking a visionary Lead AI Architect to drive our research and deployment in 2026. In this pivotal role, you will bridge the gap between theoretical research and scalable production systems, ensuring our models are safe, ethical, and capable of solving complex global challenges.

At Nexus Future Labs, we don't just predict the future of tech; we build it. You will work with a world-class team of researchers and engineers to architect the infrastructure that will power the AI landscape of tomorrow.

Responsibilities

  • Architect Scalable LLM Infrastructure: Design and implement robust, high-performance systems for training and deploying large-scale Generative AI models.
  • Optimize Model Performance: Utilize advanced quantization and pruning techniques to reduce inference costs while maximizing output quality.
  • Ethical AI Governance: Establish and enforce frameworks for AI safety, fairness, and bias mitigation in model outputs.
  • R&D Leadership: Lead experimental research into emerging paradigms such as Autonomous Agents and Multimodal Learning.
  • Technical Mentorship: Guide a team of junior data scientists and ML engineers, fostering a culture of innovation and continuous learning.

Qualifications

  • Advanced Education: PhD or Master's degree in Computer Science, Mathematics, or a related quantitative field with a focus on Deep Learning.
  • Technical Proficiency: Extensive experience with PyTorch, TensorFlow, or JAX; deep understanding of Transformer architectures.
  • Experience Level: 7+ years of experience in AI/ML engineering, with at least 3 years in a leadership or senior architect role.
  • Programming Skills: Expert-level Python proficiency and experience with distributed computing frameworks (Ray, Kubernetes).
  • Problem Solving: Demonstrated ability to tackle complex algorithmic problems and translate research papers into production-ready code.

Required Skills

Python PyTorch TensorFlow Machine Learning Generative AI LLMs Kubernetes Ray Deep Learning Natural Language Processing

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