Job Description
We are on a mission to define the technological landscape of 2026 and beyond. Nexus Horizon Solutions is seeking a visionary Senior AI Architect to lead our next-generation research and development initiatives. You will be at the forefront of building autonomous intelligent agents and scalable generative AI systems that power enterprise solutions.
In this role, you will bridge the gap between theoretical research and production-grade engineering, ensuring our AI models are robust, ethical, and scalable across distributed systems. If you are passionate about the future of Artificial General Intelligence (AGI) and want to shape the industry standards for the upcoming decade, we want to hear from you.
Why Join Us?
- Work with state-of-the-art Foundation Models (LLMs).
- Competitive equity package and health benefits.
- Flexible work environment in the heart of San Francisco.
Responsibilities
- Design and architect scalable pipelines for training, fine-tuning, and deploying Large Language Models (LLMs) and multimodal agents.
- Lead the research into emerging AI paradigms, including Reinforcement Learning from Human Feedback (RLHF) and Chain-of-Thought reasoning.
- Optimize model inference latency and reduce token costs for high-volume enterprise applications.
- Establish best practices for AI ethics, data privacy, and bias mitigation in model training.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to integrate AI capabilities into user-facing products.
- Mentor junior engineers and conduct code reviews to maintain high technical standards.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
- 8+ years of experience in software engineering with a focus on Machine Learning and Deep Learning.
- Extensive experience with Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of Transformer architectures, vector databases (e.g., Pinecone, Milvus), and vector embeddings.
- Experience with MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).
- Strong background in distributed systems and cloud platforms (AWS, GCP, or Azure).
- Proven track record of delivering production-ready AI models with measurable business impact.