Job Description
The Opportunity
Are you ready to shape the technological landscape of 2026? At FutureTech 2026, we are building the infrastructure that will power the next generation of artificial intelligence. We are seeking a visionary Senior AI Infrastructure Architect to design scalable, resilient, and high-performance systems that bridge the gap between cutting-edge research and production deployment.
As a key member of our Core Engineering team, you will define the architectural patterns for our proprietary AI platforms, ensuring they can handle millions of concurrent requests while maintaining the highest standards of data security and ethical AI governance.
Why Join Us?
- Work on mission-critical AI systems that will define the industry standard for 2026.
- Competitive compensation package with equity options.
- Flexible remote-first culture with opportunities for in-office collaboration.
Responsibilities
- Architect Scalable Systems: Design and implement distributed cloud architectures (AWS/Azure/GCP) optimized for AI workloads, ensuring high availability and fault tolerance.
- Model Deployment: Oversee the end-to-end deployment lifecycle of machine learning models, from training clusters to real-time inference APIs.
- Performance Optimization: Continuously monitor and optimize infrastructure latency, throughput, and resource utilization to support growth.
- DevOps & Automation: Build and maintain CI/CD pipelines, containerization strategies (Kubernetes/Docker), and infrastructure-as-code (Terraform) pipelines.
- AI Governance: Implement frameworks for AI ethics, data privacy, and model explainability within the infrastructure layer.
- Cross-Functional Leadership: Collaborate with data scientists, ML engineers, and product managers to translate business requirements into technical solutions.
- Disaster Recovery: Develop and test comprehensive disaster recovery plans to ensure business continuity during critical incidents.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field; PhD preferred.
- Experience: 7+ years of experience in software engineering, DevOps, or infrastructure architecture, with at least 3 years specifically focused on AI/ML infrastructure.
- Technical Skills: Deep proficiency in Python, Go, or Java; extensive experience with Kubernetes, Docker, and cloud platforms (AWS, Azure, or GCP).
- AI Stack: Experience with MLOps tools (MLflow, Kubeflow), vector databases, and large language model (LLM) deployment patterns.
- Problem Solving: Strong analytical skills with a track record of solving complex technical challenges in high-scale environments.
- Communication: Excellent written and verbal communication skills, capable of presenting technical concepts to non-technical stakeholders.
- Certifications: Relevant certifications (e.g., AWS Solutions Architect, Google Cloud Professional) are a plus.