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Ozmo

Senior Applied AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • At least 7 years of software engineering experience, including substantial experience building and operating production software-as-a-service systems.
  • At least 2 years of hands-on production large language model or agent experience, with systems shipped to real users.
  • A 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, or Gemini CLI in 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 Model Context Protocol, agent gateways, skills, memory, evaluation, observability, and interoperability.
  • Strong knowledge of retrieval-augmented generation and knowledge systems, including hybrid retrieval, vector and graph databases, metadata, taxonomy or ontology, and retrieval evaluation.
  • Strong enterprise engineering fundamentals, including multi-tenancy, security, authorization, auditability, reliability, and distributed systems.
  • Production cloud-native experience, including Kubernetes, continuous integration and continuous delivery, infrastructure as code, Docker, and AWS, Azure, or Google Cloud Platform.
  • Expert-level Python proficiency and strong software architecture skills, including API design, data modeling, testing, performance, and distributed systems.
  • Technical judgment and communication skills to explain non-deterministic AI behavior, make pragmatic tradeoffs, mentor engineers, and take ownership of outcomes.

Responsibilities

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

Desired Qualifications

  • Experience taking AI products from prototype through pilot and into production in customer-facing environments is preferred.

About the Company

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