Job Description
Are you ready to architect the digital landscape of 2026?
Apex Horizon Systems is on the hunt for a visionary AI Infrastructure Architect to lead our next-generation computing initiatives. As we push the boundaries of artificial general intelligence (AGI) and neural networking, we need a technical heavyweight who doesn't just build systems—they define the future.
In this role, you will bridge the gap between cutting-edge AI research and scalable, production-grade infrastructure. You will be responsible for ensuring our platforms are robust, secure, and ready to handle the exponential data growth expected in the coming years.
Why join us?
- Work on projects that are expected to define the industry standard for 2026.
- Competitive compensation package with equity options.
- Remote-first culture with access to top-tier talent globally.
Responsibilities
- Architect Scalable Systems: Design and implement resilient, fault-tolerant AI infrastructure capable of handling petabyte-scale data processing in real-time.
- Optimize Performance: Continuously benchmark and optimize model inference speeds and resource utilization across cloud and edge environments.
- Collaborate with Researchers: Partner with ML Engineers to translate theoretical research into deployable, high-performance software solutions.
- Security & Compliance: Implement rigorous security protocols and governance frameworks to protect sensitive data assets and ensure compliance with evolving regulations.
- Future-Proofing: Anticipate technological shifts and emerging hardware trends (e.g., quantum-ready architectures) to keep our infrastructure ahead of the curve.
- Technical Leadership: Mentor junior engineers and establish best practices for CI/CD pipelines and automated scaling.
Qualifications
- Experience: 5+ years of experience in software engineering, with at least 3 years specifically focused on AI/ML infrastructure (Kubernetes, Docker, AWS/Azure/GCP).
- Programming: Proficiency in Python, Go, or Rust, with deep understanding of data serialization formats (Protobuf, Avro).
- AI Knowledge: Strong understanding of machine learning lifecycle, model training, and deployment (MLOps) methodologies.
- Problem Solving: Demonstrated ability to troubleshoot complex, distributed system issues under pressure.
- Education: Bachelor’s degree in Computer Science, Engineering, or a related technical field; Master’s degree is a plus.
- Soft Skills: Excellent communication skills with the ability to translate technical jargon into business value for stakeholders.