Job description

About the Company: Netomi is the leading agentic AI platform for enterprise customer experience. We work with the largest global brands like Delta Airlines, MetLife, United, and others to enable agentic automation at scale across the entire customer journey. Our no-code platform delivers the fastest time to market, lowest total cost of ownership, and simple, scalable management of AI agents for any CX use case. Backed by WndrCo, Y Combinator, and Index Ventures, we help enterprises drive efficiency, lower costs, and deliver higher quality customer experiences. Want to be part of the AI revolution and transform how the world’s largest global brands do business? Join us! As a Staff Agentic AI Engineer at Netomi, you will be a senior individual contributor helping buildthe core agentic factory platform that will enable customers to agentically define and buildcustomer-specific agents to expand marketing-oriented enterprise automation and brandinteraction use cases. You will deliver innovative designs and implement reliable, scalable, andmeasurable agentic systems used by enterprise customers and business users in complex,real-world production environments. This is a hands-on engineering role for someone who drives innovation and has built productionLLM and agent systems, understands modern agent architectures, and has strong instincts forevaluation, observability, performance diagnosis, and continuous optimization. You will workacross agent orchestration, tool use, prompt and tool-selection strategies, RAG, embeddings,guardrails, and LLM evaluation systems and marry these with appropriate deterministic andML-based solutions to expand Netomi’s agentic factory. Responsibilities: Design, build, and improve production-grade AI agentic systems that optimizeweb-based and chat-based experiences leveraging grounded, structured data Architect agent workflows involving reasoning, tool use, retrieval, guardrails, escalation paths, and performance monitoring Evaluate Deep Agent, Claude Agent, OpenAI Agent, LangGraph, and other emerging agent orchestration patterns and frameworks for marketing-oriented use cases Build and maintain LLM evaluation systems, including LLM-as-judge workflows, regression evals, guardrail testing, quality metrics, and production behavior analysis Diagnose agent performance issues across prompts, tool selection, retrieval quality, latency, cost, task completion, and failure modes Design and implement RAG and embedding-based capabilities for enterprise knowledge access and automation workflows Build scalable Python services and platform components deployed in AWS cloud environments Partner with product, platform, and engineering teams to translate emerging agentic AI capabilities into reliable platform features Establish engineering best practices for continuous optimization of agentic systems Stay current with advances in LLMs, agent architectures, AI coding tools, eval methodologies, retrieval systems, and enterprise automation Requirements: Bachelor’s Degree or higher in a quantitative field (Statistics, Computer Science, Engineering, Mathematics) 5-7+ years of experience in AI/ML engineering, applied machine learning, natural language processing, and/or AI systems development 3-5 years of experience in applying AI, data science, and machine learning in the marketing space Experience leveraging data from Adobe Experience Platform, AdobeCommerce/Magento, Adobe Real-time CDP, Adobe Target, and/or AdobeAnalytics/Customer Journey Analytics for data science modeling and performance analysis Strong Python engineering skills and experience building scalable production software systems Hands-on experience with LangGraph, LangChain, or related agent orchestration frameworks Strong understanding of RAG, embeddings, retrieval quality, and knowledge-grounded generation Experience deploying or operating systems in AWS cloud environments Daily use of AI coding tools such as Codex, Claude Code, Cursor, or similar tools as part of software development workflows Strong engineering judgment, ability to work independently, and comfort operating as a senior individual contributor on ambiguous technical problems Preferred Qualifications: Master’s Degree or higher in a quantitative field (Statistics, Computer Science,Engineering, Mathematics) Experience building or using knowledge graphs for enterprise knowledge modeling,retrieval, reasoning, or personalization Hands-on experience building production LLM or AI agent systems Experience designing agent architectures involving tool use, reasoning flows, retrieval, memory, guardrails, and workflow orchestration Experience developing AI evaluation systems, including LLM-as-judge, guardrail evaluation, regression testing, and production quality measurement Experience with enterprise automation platforms, customer experience systems, workflow automation, or AI-powered business process automation Experience with observability and tracing for LLM or agent systems Experience optimizing agent systems for latency, cost, reliability, safety, and task success Experience with vector databases, search infrastructure, model routing, caching, ormulti-model architectures Netomi is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.

Requirements

Must-haves

Design, build, and improve production-grade AI agentic systems that optimizeweb-based and chat-based experiences leveraging grounded, structured data
Architect agent workflows involving reasoning, tool use, retrieval, guardrails, escalation paths, and performance monitoring
Evaluate Deep Agent, Claude Agent, OpenAI Agent, LangGraph, and other emerging agent orchestration patterns and frameworks for marketing-oriented use cases
Build and maintain LLM evaluation systems, including LLM-as-judge workflows, regression evals, guardrail testing, quality metrics, and production behavior analysis
Diagnose agent performance issues across prompts, tool selection, retrieval quality, latency, cost, task completion, and failure modes
Design and implement RAG and embedding-based capabilities for enterprise knowledge access and automation workflows
Build scalable Python services and platform components deployed in AWS cloud environments
Partner with product, platform, and engineering teams to translate emerging agentic AI capabilities into reliable platform features
Establish engineering best practices for continuous optimization of agentic systems
Stay current with advances in LLMs, agent architectures, AI coding tools, eval methodologies, retrieval systems, and enterprise automation
Bachelor’s Degree or higher in a quantitative field (Statistics, Computer Science, Engineering, Mathematics)
5-7+ years of experience in AI/ML engineering, applied machine learning, natural language processing, and/or AI systems development
3-5 years of experience in applying AI, data science, and machine learning in the marketing space
Experience leveraging data from Adobe Experience Platform, AdobeCommerce/Magento, Adobe Real-time CDP, Adobe Target, and/or AdobeAnalytics/Customer Journey Analytics for data science modeling and performance analysis
Strong Python engineering skills and experience building scalable production software systems

Nice-to-haves

Master’s Degree or higher in a quantitative field (Statistics, Computer Science,Engineering, Mathematics)
Experience building or using knowledge graphs for enterprise knowledge modeling,retrieval, reasoning, or personalization
Hands-on experience building production LLM or AI agent systems
Experience designing agent architectures involving tool use, reasoning flows, retrieval, memory, guardrails, and workflow orchestration
Experience developing AI evaluation systems, including LLM-as-judge, guardrail evaluation, regression testing, and production quality measurement
Experience with enterprise automation platforms, customer experience systems, workflow automation, or AI-powered business process automation
Experience with observability and tracing for LLM or agent systems
Experience optimizing agent systems for latency, cost, reliability, safety, and task success
Experience with vector databases, search infrastructure, model routing, caching, ormulti-model architectures

Application prep

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Keywords to mirror

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Platform: lever
Effort: quick