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Information Technology 🏒 Full Time ⭐️ Verified

Lead Generative AI Architect

Nexus Future Systems
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
16 Mei 2026
Deadline
16 Mei 2027

Job Description

We are on the bleeding edge of technological evolution. Nexus Future Systems is seeking a visionary Lead Generative AI Architect to define the roadmap for our next-generation artificial intelligence infrastructure. If you are passionate about building scalable, safe, and transformative AI systems, this is your opportunity to lead from the front.

In this role, you will bridge the gap between cutting-edge research and production-grade engineering, deploying large language models (LLMs) that power enterprise solutions worldwide.

Responsibilities

  • Architect Design: Design and implement robust, scalable, and secure architectures for Generative AI applications, including RAG pipelines, fine-tuning workflows, and multi-modal models.
  • Model Optimization: Oversee the optimization of model inference and training latency, ensuring high performance on edge and cloud environments.
  • Team Leadership: Mentor a team of MLEs and Data Scientists, fostering a culture of innovation and technical excellence.
  • Research Integration: Evaluate and integrate the latest advancements in NLP, LLMs, and Agentic AI into our product suite.
  • Security & Compliance: Establish best practices for data privacy, model security, and ethical AI usage (RLHF, Constitutional AI).
  • Stakeholder Communication: Translate complex technical concepts into strategic insights for executive leadership and product teams.

Qualifications

  • Education: Master’s or PhD in Computer Science, Machine Learning, or a related field.
  • Experience: 7+ years of experience in software engineering, with at least 4 years focused on AI/ML, Deep Learning, or NLP.
  • Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong experience with LLMs (GPT, Claude, Llama) and vector databases (Pinecone, Milvus, Weaviate).
  • Cloud Expertise: Deep understanding of cloud infrastructure (AWS, GCP, Azure) and MLOps tooling (Kubeflow, MLflow, Ray).
  • Problem Solving: Proven track record of solving complex engineering challenges in high-scale production environments.
  • Communication: Exceptional verbal and written communication skills, with the ability to lead technical discussions.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning NLP LLMs Generative AI RAG MLOps AWS GCP Cloud Computing AI Architecture

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