Job Description
We are looking for a visionary Senior AI Infrastructure Architect to lead the architectural design of our next-generation platforms, specifically tailored for the technological landscape of 2026 and beyond. In this role, you will be responsible for architecting scalable, secure, and high-performance systems that power our agentic AI solutions and next-gen data pipelines.
You will work at the intersection of deep learning optimization, edge computing, and cloud scalability. If you are passionate about defining the future of software and want to build the infrastructure that will define the era of 2026, we want to hear from you.
Why Join Us?
- Work on cutting-edge technology that defines the roadmap for 2026.
- Competitive compensation package with equity options.
- Flexible remote-first policy with access to state-of-the-art hubs.
- Opportunity to mentor a team of world-class engineers.
Responsibilities
- Design and implement resilient, distributed microservices architectures capable of handling petabyte-scale data processing for 2026 workloads.
- Lead the migration strategy to next-gen cloud-native environments, optimizing for latency, cost-efficiency, and quantum-ready protocols.
- Collaborate with ML researchers to optimize inference engines and deploy model serving infrastructure at scale.
- Establish best practices for security, observability, and compliance in AI systems.
- Drive technical decision-making and architecture reviews across cross-functional teams.
- Prototype and evaluate emerging technologies (e.g., neuromorphic computing, advanced edge AI) to stay ahead of the 2026 curve.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s degree preferred.
- 10+ years of experience in software engineering and system architecture, with at least 5 years specifically in AI/ML infrastructure.
- Deep expertise in Python, Rust, or Go, and experience with Kubernetes, Docker, and service mesh technologies.
- Proven track record of designing high-availability systems in AWS, GCP, or Azure.
- Strong understanding of machine learning frameworks (TensorFlow, PyTorch) and model optimization techniques.
- Excellent communication skills with the ability to translate complex technical concepts for diverse stakeholders.