AI Engineer embedding LLMs, RAG, and multi-agent systems into compliance training SaaS. Modernizing ASP.NET products and deploying secure AI infrastructure at startup velocity.
Responsibilities
Integrate AI capabilities such as LLM APIs, intelligent automation, and personalization into ASP.NET MVC products
Design, develop, and deploy secure, production-grade AI systems using scalable polyglot microservices
Integrate Anthropic Claude, OpenAI, and Google Gemini into SaaS platforms through fault-tolerant API routing gateways
Develop autonomous multi-agent workflows using Claude Code CLI and OpenAI Codex
Build product surfaces including smart content recommendations, automated compliance tracking, and AI-assisted reporting
Rapidly prototype, validate through adversarial testing, deploy, and iterate
Implement complex RAG pipelines and optimize massive-context hydration versus multi-stage semantic retrieval
Build and manage vector databases such as Pinecone, Weaviate, and FAISS, including persistent document embedding queues
Build polyglot microservices for long-running tasks and streaming through WebSockets and Server-Sent Events
Ensure SOC 2 compliance, data residency controls, and deterministic execution through policy-as-code agentic governance
Deploy and scale models within secure managed cloud boundaries
Identify high-impact modernization opportunities across the training and compliance platform
Migrate legacy features into scalable, AI-native architectures using agentic swarm coding and automated refactoring
Architect zero-touch CI/CD pipelines with adversarial gating and AI-driven test automation
Integrate synthetic red teaming into CI/CD to prevent prompt drift, reward hacking, and logic degradation
Translate ambiguous business problems into secure, scalable AI solutions with non-technical stakeholders
Collaborate with product and design to ship features end-to-end
Requirements
3–5+ years of enterprise software development experience with strong full-stack web fundamentals
Hands-on experience with .NET / ASP.NET MVC and C#
Fluency across Python, JavaScript, TypeScript, .NET, and NextJS
Solid understanding of relational databases; MS SQL experience is a plus
Backend experience designing polyglot APIs, decoupled async microservices, and WebSockets/SSE
Familiarity with Docker, Kubernetes, and multi-region cloud infrastructure on AWS, Azure, or GCP
Practical experience integrating AI/ML APIs or building AI-powered features in production
Enterprise LLM integration and dynamic API routing using OpenAI, Anthropic, and Gemini
Multi-agent orchestration and advanced CLI tooling, including Claude Code and OpenAI Codex
Mastery of Cursor and GitHub Copilot
RAG pipeline design, including dynamic chunking, vector databases, and embedding management
Custom evaluations with LangSmith, structured outputs, and managed fine-tuning in secure cloud environments
Zero-touch CI/CD pipelines and advanced Git workflows, including worktree isolation for autonomous sub-agents
Unit test and benchmark automation with AI-driven testing frameworks
Adversarial LLM testing and automated synthetic red-teaming
Hallucination mitigation, enterprise guardrails, and deterministic policy-as-code execution
Real-time token/credit consumption tracking and dynamic access limit monitoring
Bonus: experience with prompt engineering, RAG pipelines, or agentic workflows
Bonus: familiarity with refactoring or re-architecting legacy .NET applications
Bonus: background in HR tech, e-learning, compliance, or LMS platforms
Bonus: knowledge of SOC 2 audit requirements and compliance automation tooling
Benefits
Shape the AI strategy of a growing compliance and training platform
Own meaningful features from day one — not just tickets in a backlog
Real modernization challenge: legacy codebase + greenfield AI opportunity in parallel
Collaborative, low-ego team that ships and iterates fast
Startup velocity inside an established, well-resourced corporate portfolio
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