AI Platform Engineer
| Company | Distyl AI |
|---|---|
| Location | New York, NY, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Mid Level, Senior, Expert or higher |
Requirements
- Proficiency in Backend & Systems Engineering
- Expertise in Python, Java, Golang, or C++ for building scalable, high-performance systems
- Hands-on experience with Kubernetes, CI/CD, cloud platforms (AWS, GCP, or Azure), and infrastructure as code
- Strong interest in AI-native development, leveraging tools like ChatGPT, Claude, Perplexity, and Cursor in engineering workflows
- Experience with security, distributed systems, storage, ML/AI Ops, and large-scale observability is a plus
Responsibilities
- Build & Scale AI-Native Infrastructure: Develop and refine a platform where AI builds, optimizes, and operates AI-powered workflows. Define how AI automation integrates into traditional enterprise infrastructure.
- Develop Cloud-Native Microservices & Scalable AI Systems: Design and build secure, high-performance backend services deployed across AWS/GCP/Azure or on-prem Kubernetes environments. Build using Python, FastAPI, SQLAlchemy, Alembic, and modern DevOps tools to develop scalable, reliable AI infrastructure.
- Optimize System Performance & Reliability: Ensure high-availability, security, and observability of AI-native workflows. Implement best practices for ML/AI Ops, distributed computing, and scalable service orchestration.
- Collaborate with Cross-Functional Teams: Partner with Forward-Deployed Engineers (FDEs), AI Researchers, and business SMEs to translate real-world operational needs into platform capabilities. Advocate for strong software engineering and DevOps practices, driving high coding standards and scalable architectures.
Preferred Qualifications
- Experience with security, distributed systems, storage, ML/AI Ops, and large-scale observability is a plus