Job Description
We are seeking a visionary Principal AI & Future Tech Architect to lead our research and development division. In this pivotal role, you will define the technological roadmap for 2026 and beyond, bridging the gap between theoretical AI advancements and scalable production systems. You will be at the forefront of integrating cutting-edge machine learning models with ethical frameworks, ensuring our solutions are not only powerful but responsible.
Why Join Us?
- Shape the Future: Directly influence the technology stack that will define the next decade of AI.
- Top-Tier Compensation: Competitive salary, equity packages, and comprehensive benefits.
- Autonomy: Work in a high-performing team with a flat hierarchy and rapid decision-making processes.
The Role:
You will oversee the architecture of our neural networks, optimize deep learning pipelines, and mentor a team of elite engineers. Your work will involve navigating complex ethical landscapes in AI deployment and ensuring our systems are robust, secure, and future-proof.
Responsibilities
- Design and implement scalable, high-performance AI architectures for enterprise-level applications.
- Lead the research and integration of emerging technologies, specifically focusing on Generative AI and Large Language Models for the 2026 roadmap.
- Define technical standards and best practices for AI development, testing, and deployment.
- Mentor and guide junior architects and engineers, fostering a culture of innovation and continuous learning.
- Collaborate with cross-functional teams, including product management and legal, to ensure compliance with AI ethics and data privacy regulations.
- Conduct technical due diligence on third-party AI solutions and proprietary tools.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field with a focus on Artificial Intelligence.
- Minimum of 8-10 years of experience in software engineering, with at least 5 years specifically in AI/ML architecture.
- Deep expertise in Python, TensorFlow, PyTorch, and cloud infrastructure (AWS/GCP/Azure).
- Proven track record of deploying production-grade ML models at scale.
- Strong understanding of neural network design, NLP, and computer vision.
- Excellent communication skills, with the ability to translate complex technical concepts for non-technical stakeholders.