Senior Applied AI Engineer

What you will do:

• Build and ship production-grade agentic AI products that solve real, mission-critical enterprise problems.
• Own the full product lifecycle from discovery and prototyping through customer pilots, GA, and ongoing iteration.
• Design agentic architectures with LangGraph, LangChain, CrewAI, PydanticAI, or equivalent: planning, decomposition, tool use, multi-step workflows, memory, state management, self-correction, and human-in-the-loop patterns.
• Build the agent infrastructure layer including MCP servers/tools, skills, agent gateways, and agent-to-agent communication.
• Build the knowledge and retrieval layer including RAG, hybrid graph/vector retrieval, taxonomy, ontology, metadata, provenance, and content lifecycle management.
• Design for enterprise-grade security and trust, including authorization, tenant isolation, auditability, guardrails, prompt-injection protection, and controlled tool access.
• Build rigorous evaluation and observability systems that measure task success, quality, latency, cost, drift, and reliability. Use them to catch regressions before release and monitor performance in production.
• Make sound architectural tradeoffs about when to use agents versus deterministic software, balancing accuracy, reliability, latency, complexity, and cost.
• Own systems in production end-to-end, including deployment, monitoring, incident response, performance, reliability, and cost.
• Create reusable patterns and tooling, mentor other engineers, and help establish how Ozmo builds with AI.

What you will bring to the role:

• 7+ years of software engineering experience, with substantial experience building and operating production SaaS.
• 2+ years of hands-on production LLM/agent experience, with demonstrated systems shipped to real users.
• Proven track record of taking AI products from prototype through pilot and into production, ideally in customer-facing environments.
• Practical experience using AI coding agents such as Claude Code, Codex, Cursor, Gemini CLI, or similar tools as part of your day-to-day development.
• Deep hands-on experience building agentic systems, including tool use, orchestration, planning, multi-agent workflows, and human-in-the-loop systems.
• Strong understanding of AI infrastructure, particularly MCP, agent gateways, skills, memory, evaluation, observability, and interoperability.
• Strong knowledge of RAG and knowledge systems, including hybrid retrieval, vector + graph databases, metadata, taxonomy/ontology, and retrieval evaluation.
• Strong enterprise engineering fundamentals, including multi-tenancy, security, authorization, auditability, reliability, and distributed systems.
• Production cloud-native experience, including Kubernetes, CI/CD, infrastructure-as-code, Docker, and AWS/Azure/GCP.