Job Description
Are you ready to define the future?
OmniStream AI is pioneering the next generation of intelligent systems. As we prepare for the transformative leap into 2026, we are looking for a visionary Senior AI Architect to lead our research and engineering division. You will be at the forefront of developing scalable, ethical, and high-performance artificial intelligence solutions that will power the enterprise of tomorrow.
In this role, you won't just be maintaining legacy systems; you will architect the infrastructure for the next decade of technological evolution. If you are passionate about Generative AI, Large Language Models (LLMs), and ethical data practices, we want to meet you.
Why Join Us?
- Impact: Work on projects that will shape the industry standard for AI integration by 2026.
- Equity: Competitive stock options in a high-growth unicorn.
- Flexibility: Hybrid work model with a state-of-the-art office in the heart of San Francisco.
- Growth: Access to the latest hardware and training from industry pioneers.
Responsibilities
- Architect and design end-to-end machine learning pipelines and scalable AI infrastructure capable of handling petabyte-scale data.
- Lead the R&D strategy for Generative AI models, ensuring deployment readiness for the 2026 market landscape.
- Collaborate with cross-functional teams (Product, Engineering, Legal) to implement AI safety and ethical guidelines.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Optimize model inference speed and reduce latency to ensure real-time user experiences.
- Conduct rigorous code reviews and establish best practices for reproducibility and version control.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- 10+ years of experience in software engineering, with at least 5 years focused on AI/ML architecture.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven experience deploying LLMs and Generative AI models at scale in production environments.
- Strong understanding of distributed systems, cloud architecture (AWS/Azure/GCP), and containerization (Kubernetes/Docker).
- Experience with MLOps tools (MLflow, Airflow) and data orchestration platforms.