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Senior AI Architect (Generative AI)

OmniFuture Technologies
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
28 Juni 2026
Deadline
28 Jun 2027

Job Description

Are you ready to shape the future of Artificial Intelligence in 2026?

At OmniFuture Technologies, we are at the forefront of the Generative AI revolution. We are building the next generation of intelligent systems that will redefine human-computer interaction. We are seeking a visionary Senior AI Architect to lead our cutting-edge research and deployment of Large Language Models (LLMs) and autonomous agents.

In this role, you will not just maintain legacy systems; you will architect the foundational infrastructure for tomorrow’s breakthroughs. If you are passionate about pushing the boundaries of what is possible with Machine Learning and possess a deep understanding of the AI stack, we want to meet you.

Responsibilities

  • Architect Scalable AI Systems: Design and deploy robust, high-performance Generative AI infrastructures capable of handling billions of parameters.
  • Model Optimization: Lead efforts in fine-tuning, pruning, and distilling LLMs to reduce latency and cost while maximizing accuracy.
  • RAG & Vector Database Management: Design advanced Retrieval-Augmented Generation (RAG) pipelines to ensure context-aware and factual AI responses.
  • Collaborative Engineering: Partner with software engineers and product managers to integrate AI models into scalable web and mobile applications.
  • Research & Innovation: Stay ahead of the curve with the latest advancements in AI, including Multimodal models and Agentic workflows.
  • Code Review & Mentorship: Mentor junior data scientists and engineers, establishing best practices for MLOps and model governance.

Qualifications

  • Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
  • Experience: 5+ years of professional experience in Deep Learning, NLP, or AI Engineering.
  • Technical Proficiency: Strong expertise in Python, PyTorch, TensorFlow, or JAX.
  • LLM Knowledge: Hands-on experience with Hugging Face, LangChain, and fine-tuning open-source models (e.g., Llama, Mistral).
  • Infrastructure: Experience with cloud platforms (AWS/GCP/Azure), Docker, Kubernetes, and vector databases (Pinecone, Milvus, Weaviate).
  • Problem Solving: Ability to troubleshoot complex distributed systems and optimize model inference at scale.

Required Skills

Generative AI Machine Learning Deep Learning NLP Python PyTorch TensorFlow LLM Large Language Models MLOps RAG Retrieval-Augmented Generation Docker Kubernetes AWS GCP Data Science AI Engineering

Ready to Take This Challenge?

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