Job Description
We are seeking a visionary 2026 AI Architect to lead the design and implementation of our next-generation artificial intelligence infrastructure. In this pivotal role, you will bridge the gap between theoretical machine learning advancements and practical, scalable engineering solutions. As we prepare for the technological landscape of 2026, you will be at the forefront of integrating Generative AI, predictive analytics, and autonomous systems into our core product ecosystem.
You will collaborate with a diverse team of data scientists, engineers, and product managers to define the technical vision for our AI roadmap. If you are passionate about building systems that think, learn, and adapt, and you want to shape the future of technology, we want to hear from you.
Why Join Us?
- Competitive salary and equity package.
- Work with cutting-edge technology in a forward-thinking environment.
- Flexible remote and hybrid work options.
Responsibilities
- Architect and design scalable, high-performance machine learning infrastructure capable of handling petabyte-scale data.
- Lead the research and implementation of emerging AI technologies, specifically focusing on Large Language Models (LLMs) and neural networks.
- Define and maintain the technical roadmap for AI integration, ensuring alignment with business goals and 2026 industry standards.
- Optimize existing data pipelines and model inference times to improve system efficiency and reduce latency.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and innovation.
- Evaluate and integrate third-party AI APIs and open-source frameworks to enhance product capabilities.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field; PhD preferred.
- Minimum of 5 years of experience in software engineering, with at least 3 years specializing in AI/ML architecture.
- Proficiency in programming languages such as Python, C++, or Java, with deep knowledge of frameworks like TensorFlow, PyTorch, or JAX.
- Strong understanding of cloud platforms (AWS, Azure, or Google Cloud) and containerization technologies (Docker, Kubernetes).
- Experience designing MLOps pipelines for model training, deployment, and monitoring.
- Excellent problem-solving skills and the ability to translate complex business requirements into technical specifications.