Job Description
Architect the Future of AI
We are seeking a visionary 2026 Synthetic Intelligence Architect to lead the next generation of autonomous systems. As the AI landscape rapidly evolves, the demand for high-fidelity synthetic data and robust evaluation frameworks is at an all-time high. In this role, you will define the architectural blueprint for our next-generation Large Language Models (LLMs) and autonomous agents, ensuring they are ready for the demands of 2026 and beyond.
You will bridge the gap between theoretical AI research and production-grade engineering, building systems that are scalable, secure, and adaptable to the future of synthetic intelligence.
Responsibilities
- Design Synthetic Data Pipelines: Architect scalable frameworks for generating high-fidelity synthetic data to train and evaluate AI models without compromising privacy.
- Develop LLM Evaluation Frameworks: Create rigorous testing protocols and metrics to measure model performance against human benchmarks and safety guidelines.
- System Architecture & Scalability: Design distributed system architectures capable of handling petabytes of data and supporting real-time inference at scale.
- Collaborate with Research Teams: Partner with data scientists and researchers to translate breakthroughs in generative AI into practical, deployable synthetic data strategies.
- Future-Proofing: Anticipate emerging trends in AI alignment and safety, integrating them into the core system design to ensure long-term viability.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related field (PhD preferred).
- Experience: 5+ years of experience in AI engineering, data science, or software architecture, with a strong focus on Generative AI or Large Language Models.
- Technical Skills: Deep proficiency in Python, PyTorch, TensorFlow, and experience with synthetic data generation libraries (e.g., SDG, Hugging Face Transformers).
- System Design: Proven track record of designing large-scale distributed systems using cloud-native technologies (AWS, GCP, or Azure).
- Innovation: Demonstrated ability to think critically about the long-term implications of AI technologies and propose innovative solutions.