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# Founding AI Engineer

- **Company:** NOSCEN INC.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Systems Engineering, Microsoft Azure, Python (Programming Language), Open Source Technology, Large Language Models, Multi-Agent Systems, IT Architecture, Backend, Kubernetes, Virtual Agents, Docker
- **Published:** June 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a32c80a488f92682

## About the Role

Do you have experience in Teamwork?, Do you have a Bachelor's degree?, * Bachelor's degree or above in CS or a related field

 * At least 3 years of full-time work experience in related field
 * At least 2 year of Gen AI experience building and shipping applied generative AI systems end to end
 * Hands-on experience building and shipping AI systems with LLMs, VLMs, or multimodal models, including the ability to run, adapt, and evaluate open-source models such as DeepSeek, Qwen, Llama, or similar.
 * Deep understanding of context engineering - RAG, memory systems, embeddings, retrieval/ranking, MCP servers, agent frameworks, or other ways of making models reason over context they did not see in pretraining.
 * Strong Python and backend/systems engineering ability. You can stand up pipelines yourself and take ambiguous ideas all the way to working systems.
 * Action-driven: when you hear a vague idea, your hands start moving.
 * Absolute ownership in a fast-paced and high-intensity start-up environment
 * Clarity of thought, excellent communication

Bonus

 * Experience with Docker, Kubernetes, or major cloud platforms such as AWS, GCP, or Azure.
 * Early-stage start up experience

## Description

As a Founding AI Engineer, you will own the entire intelligence layer of this vision from scratch. You will drive the end-to-end implementation of a highly personalized AI assistant across multimodal signals, context engineering, memory, and actions, while shaping an AI architecture that fiercely protects user privacy and data boundaries.

 * Multimodal Context Understanding: Build AI systems that understand real-world user context from multimodal inputs
 * Memory & Personalization Infrastructure: Design context and memory systems that support deep user insights and personalization over time.
 * Retrieval & Reasoning: Develop systems that reason over the right context at the right moment to help the product assist users timely and usefully.
 * Productization of AI Pipeline: Build and own the pipelines, model workflows, APIs, storage, evaluation, monitoring, and privacy-first data handling behind all of it.
 * Cross-function Collaboration: Work closely with product, design, and engineering to turn ambiguous human needs into robust AI capabilities.


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