Job Description
The Future of Intelligence Starts Here.
At Apex Horizon Systems, we are not just building software; we are architecting the intelligence layer for the next decade. We are looking for a visionary Lead AI Architect to define the technical roadmap for our 2026 strategic initiatives. If you thrive on solving complex, unsolved problems at the intersection of Large Language Models (LLMs), generative AI, and real-time decision systems, we want to meet you.
In this role, you will lead a world-class team of researchers and engineers, pushing the boundaries of what is possible with artificial intelligence. You will be responsible for the full lifecycle of our AI products, from experimental research to production-grade deployment, ensuring scalability, security, and ethical alignment.
Why Join Us?
- Work on cutting-edge technology that will define the 2026 landscape.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a hub in San Francisco.
- Access to top-tier compute resources and research data.
Responsibilities
- Architect and implement scalable machine learning pipelines capable of handling petabyte-scale data.
- Lead the research and development of next-generation generative AI models and agentic workflows.
- Optimize model inference latency and cost efficiency for real-time applications.
- Establish best practices for MLOps, including CI/CD, monitoring, and model versioning.
- Collaborate with cross-functional teams to translate business requirements into technical AI solutions.
- Ensure all AI systems adhere to strict ethical guidelines and data privacy regulations.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on Artificial Intelligence or Machine Learning.
- 7+ years of professional experience in AI/ML engineering, with at least 3 years in a leadership or architecture role.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven experience working with LLMs (Hugging Face, LangChain) and fine-tuning techniques (LoRA, QLoRA).
- Strong understanding of distributed systems, cloud architecture (AWS/GCP), and containerization (Docker/Kubernetes).
- Experience deploying AI models to production environments and optimizing for performance.