Senior Applied AI Engineer

(united States)Remotesenior

OtherApplied Ai Engineer

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Quick Summary

Key Responsibilities

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,

Requirements Summary

AZ, CA, CO, DC, FL, GA, IL, NC, NJ, SC, TN, TX, VA, WA, WI. Unfortunately, candidates who do not live in one of the listed states will not be considered.

Technical Tools

OtherApplied Ai Engineer

Ozmo is transforming how enterprises use AI to automate complex support at scale. We’re growing a team of engineers who enjoy hard problems, ship production systems, and know how to apply AI inside enterprise SaaS.

We’re looking for a Senior Applied AI Engineer to help build our next generation of products: agentic systems that solve important, real-world problems for enterprise customers.

You’ll build the agentic core and the infrastructure required to run it safely in an enterprise environment. That includes MCP servers and skills, memory and knowledge systems, identity, guardrails, and observability. You’ll make practical tradeoffs around model behavior, latency, reliability, and cost. You should also know when an agent isn’t the right solution.

You’ll use AI coding agents throughout the development process while retaining full ownership of the quality of what ships.

If you want to build agentic systems that work in production and help shape how Ozmo builds them, we’d like to talk.

Responsibilities

~2 min read

→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.

→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.
→Expert-level Python and strong software architecture skills, including API design, data modeling, testing, performance, and distributed systems.
→Exceptional technical judgment and communication, with the ability to explain non-deterministic AI behavior, make pragmatic tradeoffs, mentor engineers, and take ownership of outcomes.