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Information Technology 🏢 Full Time ⭐️ Verified

Lead AI Architect: Shaping the Future of Intelligence (2026)

FutureScale Technologies
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

The 2026 Vision

We are at the forefront of the Artificial Intelligence revolution. As we accelerate towards the 2026 roadmap, we are building the next generation of Autonomous Agents and Multimodal LLMs. We are looking for a visionary Lead AI Architect to design the infrastructure that will power the intelligence of tomorrow.

Your Mission

You will lead the engineering team in developing scalable, safe, and efficient Generative AI systems. You will bridge the gap between theoretical research and production-grade deployment, ensuring our AI solutions are robust, explainable, and aligned with human values.

Responsibilities

  • Architect and deploy large-scale LLM and Generative AI infrastructures using modern MLOps practices.
  • Lead the design of RAG (Retrieval-Augmented Generation) pipelines to enhance model accuracy and reduce hallucinations.
  • Oversee the fine-tuning and fine-graining of open-source models (e.g., Llama 3, Mistral) for specific enterprise use cases.
  • Implement robust evaluation frameworks and monitoring systems to ensure model safety and performance in production.
  • Collaborate with product and research teams to translate advanced AI concepts into actionable roadmaps for 2026.
  • Drive the adoption of best practices in ethical AI governance and data privacy.

Qualifications

  • Bachelor’s degree in Computer Science, Mathematics, or a related field; Master’s or PhD preferred.
  • 5+ years of experience in machine learning engineering, with at least 2 years leading high-impact AI projects.
  • Deep expertise in Python, PyTorch, or TensorFlow.
  • Proven experience designing and deploying LLMs (e.g., GPT-4, Claude, Llama) and Agentic workflows.
  • Strong understanding of distributed systems, containerization (Docker/Kubernetes), and cloud platforms (AWS/GCP/Azure).
  • Experience with vector databases (Pinecone, Milvus, Weaviate) and vector search optimization.
  • Familiarity with MLOps tools (MLflow, Kubeflow, Airflow).

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning LLMs Generative AI MLOps Kubernetes Docker AWS RAG NLP Data Engineering

Ready to Take This Challenge?

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