Job Description
We are seeking a visionary Senior AI Architect to lead our cutting-edge initiatives for the 2026 roadmap. As a pioneer in next-generation technology, we are building the infrastructure that will define the future of artificial intelligence. You will be responsible for architecting scalable, high-performance machine learning systems that power our core products. If you are passionate about pushing the boundaries of what is possible in AI and want to shape the technology landscape of 2026, we want to meet you.
Why Join Us?
- Work on mission-critical AI infrastructure for the 2026 era.
- Competitive compensation and equity package.
- Flexible remote-first culture with premium benefits.
- Opportunity to mentor top-tier engineering talent.
Responsibilities
- Architect Scalable ML Systems: Design and implement robust, fault-tolerant machine learning pipelines and neural network architectures optimized for enterprise scale.
- Lead 2026 Innovation: Drive research and development in Generative AI and Large Language Models (LLMs) to define our product strategy for the upcoming years.
- Model Optimization: Continuously refactor and optimize existing models to reduce latency, improve inference speeds, and lower operational costs.
- Cross-Functional Collaboration: Partner with product managers and data scientists to translate complex business requirements into technical AI solutions.
- Technical Mentorship: Guide junior engineers and data scientists, fostering a culture of technical excellence and innovation within the team.
- Cloud Infrastructure: Oversee the deployment of models on cloud platforms (AWS/GCP) ensuring high availability and security standards.
- R&D Leadership: Stay ahead of the curve on emerging AI trends to integrate breakthrough technologies into our 2026 roadmap.
Qualifications
- Education: Master’s degree in Computer Science, Artificial Intelligence, or a related technical field; PhD preferred.
- Experience: Minimum of 6-8 years of experience in software engineering, with at least 4 years specifically in machine learning and deep learning.
- Programming: Expert proficiency in Python and C++ with a deep understanding of software engineering best practices.
- Frameworks: Strong experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Cloud & Tools: Proven track record of deploying models on cloud infrastructure (AWS, GCP, or Azure) and using containerization (Docker, Kubernetes).
- Problem Solving: Ability to debug complex distributed systems and optimize algorithmic efficiency.
- Communication: Excellent written and verbal communication skills for presenting technical concepts to non-technical stakeholders.