StealthAI builds “invisible” AI layers that embed into enterprise systems without exposing sensitive data.
Its models operate under strict privacy constraints using encrypted and federated inference pipelines.
Designed for sectors like finance and healthcare, StealthAI enables secure AI integration with zero data leakage.
You'll be a good fit if you have
10–15 years of experience in backend or platform engineering, with at least 5 years of owning and operating distributed systems at scale.
Proven success architecting and delivering B2B SaaS and fintech platforms in startup or high-growth environments.
Deep expertise in scalability and reliability fundamentals — including concurrency control, backpressure handling, idempotency, circuit breaking, and fault isolation.
Hands-on experience (2+ years) with LLM technologies, having built production-grade features using OpenAI, Anthropic, or open-source models.
Strong understanding of retrieval pipelines, vector databases, prompt orchestration, evaluation frameworks, safety mechanisms, and cost optimization for LLM workloads.
Experience integrating modern systems with legacy enterprise stacks (COBOL, mainframes), including managing batch jobs, message queues, and complex data flows.
A solid grasp of event-driven architectures, stream processing, and microservice design principles.
Strong ability to design and enforce production controls around performance, cost, and data safety for AI workloads.
Excellent communication and stakeholder management skills — able to translate business, risk, and compliance needs into scalable, reliable architectures.
(Bonus) Familiarity with PCI DSS, SOC 2, RBI, or similar regulatory frameworks, and experience with event-sourced or CQRS architectures in regulated environments.
A hands-on, IC mindset with the ability to prototype, guide implementation, and mentor senior engineers while setting high architectural standards.
You are pre-screened
Team Round1's take
StealthAI’s deployment is zero-disruption — install it once and it runs invisibly within enterprise environments.
Delivers explainable, privacy-preserving AI insights across sectors handling sensitive data.
Early pilots with financial and healthcare partners report 20–30% uplift in accuracy while maintaining compliance.