Principal Applied Scientist, Aws Agentic AI

Amazon Amazon · Big Tech · NY +1 · Applied Science

Principal Applied Scientist role at AWS focusing on Agentic AI for an enterprise generative AI assistant. Responsibilities include leading research and development in generative AI and Agentic AI, building and optimizing multi-modal foundation models, training and fine-tuning LLMs, and architecting scalable systems. The role involves bringing research into production and enabling intelligent agents for complex reasoning and workflow automation.

What you'd actually do

  1. You’ll work on building and optimizing multi-modal foundation models, training and fine-tuning state-of-the-art LLMs, and architecting systems that scale efficiently across domains.
  2. This role blends science leadership, hands-on innovation, and deep collaboration with engineering teams to bring research into production.
  3. lead research and development efforts in generative AI and Agentic AI to enable intelligent agents that perform complex reasoning, automate multi-step workflows, and make enterprise users significantly more productive.

Skills

Required

  • PhD in Machine Learning, Computer Science, Electrical Engineering, or a related technical field OR a Master’s degree with 5+ years of relevant industry or research experience.
  • Industry experience developing machine learning models for real-world applications.
  • Experience with generative AI, including model training or building systems with pre-trained foundation models.
  • Proficiency in Python or similar programming languages.
  • Experience in at least one of the following areas: natural language processing (NLP), large language models (LLMs), computer vision, or Agentic AI.

Nice to have

  • Experience applying generative AI to enterprise or multi-modal tasks (e.g., code generation, document understanding, or task planning).
  • Strong understanding of agentic architectures, autonomous systems, or task orchestration.
  • Hands-on experience with scalable ML infrastructure, distributed training, or optimization of large models.
  • Deep knowledge of AI safety, hallucination mitigation, or retrieval-augmented generation (RAG).
  • Experience mentoring junior scientists and influencing cross-functional stakeholders.
  • Ability to think strategically and communicate complex technical topics to non-experts, including senior leadership.

What the JD emphasized

  • Proven record of peer-reviewed publications or granted patents in AI/ML.
  • Track record of shipping scientific innovations into customer-facing products at scale.

Other signals

  • building and optimizing multi-modal foundation models
  • training and fine-tuning state-of-the-art LLMs
  • architecting systems that scale efficiently across domains
  • intelligent agents that perform complex reasoning
  • automate multi-step workflows