Job Description
At 2026, we are building the infrastructure for the next generation of intelligence. We are looking for a visionary Senior AI Solutions Architect to lead our technical strategy and design scalable, cutting-edge machine learning systems. If you are passionate about the intersection of data science and cloud infrastructure and want to shape the future of technology, we want to hear from you.
Why Join Us?
- Work on high-impact projects that define industry standards.
- Competitive compensation package with equity opportunities.
- Flexible remote-first culture with a focus on work-life balance.
- Access to state-of-the-art hardware and research resources.
We are looking for a self-starter who thrives in a fast-paced environment and can translate complex business requirements into robust technical solutions.
Responsibilities
- Architectural Leadership: Design and oversee the implementation of scalable AI and machine learning pipelines and infrastructure.
- Technical Strategy: Define the long-term technical vision for AI solutions, ensuring alignment with business goals.
- System Optimization: Continuously monitor and optimize model performance, latency, and cost efficiency in production environments.
- Cross-Functional Collaboration: Work closely with data scientists, software engineers, and product managers to integrate AI models into core products.
- Team Mentorship: Guide junior architects and engineers, fostering a culture of innovation and technical excellence.
- Compliance & Security: Ensure all AI systems adhere to industry standards for data privacy, security, and ethical AI practices.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field (PhD preferred).
- Experience: Minimum of 8+ years of experience in software engineering, with at least 4+ years specifically in AI/ML architecture.
- Technical Skills: Proficiency in Python, TensorFlow, PyTorch, or similar deep learning frameworks.
- Cloud Expertise: Strong experience designing on cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure.
- Distributed Systems: Deep understanding of distributed systems, microservices architecture, and containerization (Docker, Kubernetes).
- Problem Solving: Proven track record of solving complex technical challenges and driving architectural decisions.