Job Description
We are seeking a visionary Senior AI Infrastructure Architect to define the technical roadmap for our 2026 and beyond initiatives. In this pivotal role, you will bridge the gap between cutting-edge AI research and scalable, production-grade engineering. You will lead the design of resilient, high-performance systems capable of handling next-generation generative models and large-scale data processing.
As a key strategist, you will collaborate with cross-functional teams to integrate AI seamlessly into our core products, ensuring performance, security, and scalability. Join us in shaping the technological landscape of the future.
Why Join Us?
- Work on pioneering AI infrastructure that scales globally.
- Competitive compensation package and equity options.
- Flexible remote-first culture with access to top-tier tech resources.
Responsibilities
- Architect Future-Proof Systems: Design and implement scalable infrastructure architectures optimized for large language models (LLMs) and AI inference pipelines.
- Roadmap Planning: Lead the technical strategy for 2026, evaluating emerging technologies like quantum computing interfaces and edge AI deployment.
- System Optimization: Oversee performance tuning, load balancing, and resource allocation to ensure 99.99% uptime for critical AI services.
- Cloud Strategy: Direct the migration and management of cloud-native environments (AWS, GCP, Azure) with a focus on cost-efficiency and security.
- Mentorship: Lead a team of engineers, conducting code reviews and fostering a culture of continuous learning and innovation.
- Disaster Recovery: Develop and maintain robust disaster recovery plans to protect sensitive data and model assets.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 8+ years of experience in software architecture, with at least 3 years specifically focused on AI/ML infrastructure or high-scale distributed systems.
- Technical Skills: Proficiency in Python, Kubernetes, Docker, and SQL/NoSQL databases. Deep understanding of GPU clusters and tensor processing units.
- AI Expertise: Hands-on experience deploying and optimizing LLMs (e.g., GPT, Claude) and integrating Retrieval-Augmented Generation (RAG) architectures.
- Soft Skills: Exceptional communication skills with the ability to translate complex technical concepts to non-technical stakeholders.
- Certifications: AWS Solutions Architect Professional or Google Professional Machine Learning Engineer preferred.