AI Infrastructure Engineer
We’re looking for an AI Infrastructure Engineer to build and maintain the scalable systems, tooling, and pipelines that support Quantix’s advanced machine learning and predictive intelligence stack.
About the position
Quantix is seeking an AI Infrastructure Engineer to architect, optimize, and maintain the systems that power our AI, ML, and real-time intelligence workflows. You will work across model serving, distributed compute, data pipelines, and automation tooling — ensuring reliability, speed, and scalability for teams building predictive models and institutional-grade market intelligence. This role is engineering-heavy, hands-on, and central to the performance of Quantix’s analytics ecosystem.
Job responsibilities
- Build and maintain scalable infrastructure for training, deploying, and monitoring machine learning models.
- Develop high-performance data pipelines to move, transform, and process large datasets efficiently.
- Create tools and automated workflows for model training, continuous deployment, and experiment tracking.
- Optimize compute systems (GPU clusters, cloud services, distributed environments) for ML workloads.
- Ensure reliable, low-latency model serving across internal and client-facing systems.
- Work with researchers and ML engineers to productionize new models and support rollout at scale.
- Monitor system performance, diagnose bottlenecks, and implement performance improvements.
- Maintain CI/CD pipelines tailored for machine learning and data science teams.
- Develop internal APIs, services, and infrastructure components to support AI-driven products.
Required skills
- Strong experience with Python, Bash, and backend engineering fundamentals.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and scalable compute environments.
- Familiarity with containerization (Docker), orchestration (Kubernetes), and distributed systems.
- Understanding of ML infrastructure tools such as Ray, MLflow, Airflow, or similar.
- Experience building data pipelines, ETL workflows, or streaming systems.
- Ability to optimize model serving, inference speed, and system-level performance.
- Strong understanding of CI/CD workflows for ML and data applications.
- Experience with monitoring, logging, performance analytics, or system observability tools.
- Bonus: familiarity with GPU optimization, tensor runtimes, or high-performance computing.
Benefits
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