Job Description
Shape the Future of AI with Project 2026
At QuantumCore Systems, we are not just building software; we are architecting the neural backbone of the next decade. We are currently seeking a visionary Senior AI Infrastructure Engineer to spearhead the Project 2026 initiative. In this pivotal role, you will bridge the gap between cutting-edge artificial intelligence and scalable, high-performance cloud infrastructure. If you thrive in a fast-paced, high-impact environment and want to define the standards for AI deployment in 2026 and beyond, we want to hear from you.
Why Join Us?
- Impact: Directly influence the roadmap that will define enterprise AI for years to come.
- Flexibility: Hybrid work model with a competitive remote-first culture.
- Compensation: Top-tier salary and equity package.
Responsibilities
- Design and architect scalable, fault-tolerant cloud-native infrastructure for Project 2026, utilizing Kubernetes and microservices patterns.
- Lead the integration of advanced Machine Learning models into production pipelines, optimizing for latency and throughput.
- Implement rigorous security protocols and data governance frameworks to ensure compliance and protect intellectual property.
- Collaborate cross-functionally with data scientists and software engineers to translate research into deployable code.
- Drive the migration of legacy systems to modern, serverless architectures to improve operational efficiency.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards across the team.
- Monitor system health and performance, utilizing AIOps tools to predict and resolve infrastructure bottlenecks.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field; 8+ years of experience in software engineering and systems architecture.
- Deep expertise in Python, Go, or Rust, with proven experience deploying large-scale applications on AWS or GCP.
- Strong proficiency in containerization technologies (Docker, Kubernetes) and CI/CD pipelines.
- Experience with MLOps platforms (e.g., Kubeflow, MLflow) and distributed computing frameworks (Apache Spark, Ray).
- Familiarity with AI/ML model serving technologies and hardware acceleration (GPUs/TPUs).
- Excellent problem-solving skills and the ability to thrive in ambiguous, fast-evolving environments.