Position Overview

Application Software Engineers in this role build vertical AI agent systems that validate and prove the underlying hardware and software capabilities through real-world enterprise deployments. You will operate simultaneously as a product engineer (building compelling agent applications), a platform engineer (stress-testing hardware through realistic workloads), and a security engineer (implementing privacy-preserving inference via FHE hardware acceleration) to influence requirements conversations and go-to-market efforts in regulated sectors such as healthcare and finance.

Key Responsibilities

▸ Design and build enterprise-grade multi-agent AI workflows in target verticals (healthcare, finance, legal) using LangGraph, CrewAI, and/or AutoGen, optimized to leverage advanced CXL memory and accelerator hardware.

▸ Architect agent systems that exploit ultra-long context windows (1M+ tokens) backed by multi-terabyte memory, enabling persistent long-term agent memory and cross-session state beyond typical GPU-based limits.

▸ Implement privacy-preserving inference pipelines using FHE hardware acceleration so agents can process sensitive data (e.g., medical or financial) without exposing plaintext to the inference engine.

▸ Build the agent workload benchmark suite used to validate hardware performance metrics (TTFT, throughput, KV-cache utilization) in coordination with the system software team.

▸ Develop agent security hardening features: least-privilege tool access, skill signing and attestation, behavioral anomaly detection, and prompt-injection defenses aligned with OWASP Top-10 LLM risks.

▸ Prototype multi-agent orchestration for reference deployments in priority regions, validating real multi-agent workloads on the platform.

▸ Collaborate with the system software team to surface agent-level requirements for KV-cache and OKC-style APIs, closing the feedback loop between application behavior and hardware optimization.

▸ Create technical documentation, reference architectures, and integration guides for enterprise and hyperscaler partners.

Required Skills & Experience

▸ 6+ years in software engineering, including 2+ years building production multi-agent or agentic AI systems.

▸ Hands-on proficiency with at least two of: LangGraph, CrewAI, AutoGen, LlamaIndex Workflows, or comparable multi-agent orchestration frameworks.

▸ Demonstrated experience validating or benchmarking AI hardware through real-world agent workloads rather than only synthetic benchmarks.

▸ Strong Python engineering skills and experience deploying LLM inference services in containerized / cloud-native environments (e.g., vLLM, SGLang, Triton).

▸ Deep understanding of 1M+ token context optimization challenges: memory management, chunked processing, hierarchical summarization, and large-scale RAG.

▸ Working knowledge of FHE concepts and privacy-preserving ML, with the ability to integrate hardware-accelerated FHE libraries (such as OpenFHE or Concrete-ML) into inference pipelines.

▸ Familiarity with the AI agent security threat landscape: prompt injection, tool misuse, credential theft, and multi-turn escalation-style attacks.

Preferred Qualifications

▸ Domain experience in healthcare AI (e.g., HIPAA/HITRUST), financial services AI (e.g., SOX, GLBA), or other regulated enterprise AI environments.

▸ Experience integrating AI agents with enterprise data systems (EHR/EMR, CRM, ERP) and defining production-ready tool schemas.

▸ Background in AI red-teaming, adversarial prompting, or LLM security research.

▸ Familiarity with regional enterprise or healthcare market dynamics relevant to key go-to-market partnerships.