Job Description
Are you ready to define the future of Artificial Intelligence?
Nexus Future Systems is pioneering the next generation of Agentic AI. As a Senior AI Architect (2026 Focus), you will lead the design and deployment of scalable, autonomous machine learning systems that redefine industry standards. Join us in building the intelligence layer for the world's most complex challenges.
We are looking for a visionary engineer who doesn't just write code but architects ecosystems. If you are passionate about Large Language Models (LLMs), Reinforcement Learning, and building systems that think, we want to hear from you.
Why Nexus Future Systems?
- Work on cutting-edge projects for the 2026 roadmap.
- Competitive equity package and top-tier benefits.
- Flexible remote-first culture with hubs in SF and NYC.
Responsibilities
- Architect & Deploy: Design and implement high-performance, scalable AI architectures for next-generation language models and agents.
- Optimization: Lead initiatives to optimize model inference speed and reduce latency in real-time applications.
- Research Integration: Bridge the gap between theoretical research and production-grade code; integrate novel algorithms into the core engine.
- System Design: Oversee the full ML lifecycle, from data ingestion and feature engineering to model training, validation, and monitoring.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Collaboration: Partner with product and engineering teams to translate business requirements into technical AI solutions.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related field (or equivalent extensive experience).
- Experience: 5+ years of professional experience in machine learning engineering, with a focus on NLP or Generative AI.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and modern deep learning frameworks.
- Cloud Mastery: Strong experience deploying models on cloud platforms (AWS, GCP, or Azure) using Kubernetes and Docker.
- Agentic AI: Deep understanding of LLMs, RAG (Retrieval-Augmented Generation), and prompt engineering strategies.
- Problem Solving: Proven ability to troubleshoot complex system bottlenecks and optimize large-scale data pipelines.