Netomi: Staff Agentic AI Engineer - Marketing — Toronto/North America (Remote)

About Netomi

Netomi develops an agentic-AI platform for enterprise customer experience. Its employer introduction describes no-code tools for creating and managing AI agents across customer interactions. The platform connects automation with the practical workflows used by customer-service organisations. Recruitment material also describes agentic workflows, prompt engineering and API-based automation around that platform. The business is therefore concerned with how AI agents perform tasks within customer journeys, as well as the software used to build and operate them.

The position and its setting

Netomi lists Staff Agentic AI Engineer - Marketing in Toronto/North America (Remote), under reference 60fa8c9b-e031-476e-a9d3-011966c4b495. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Product Engineering
• Employment type: Full Time - Remote
• team: Product Development

Responsibilities and work described

• Design, build, and improve production-grade AI agentic systems that optimizeweb-based and chat-based experiences leveraging grounded, structured data
• Architecture: 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
• Building: 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
• Building: 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
• Establishment: 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

Essential requirements and candidate background

• 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
• Python engineering skills and experience building scalable production software systems
• Experience: LangGraph, LangChain, or related agent orchestration frameworks
• Understanding: 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
• Engineering judgment, ability to work independently, and comfort operating as a senior individual contributor on ambiguous technical problems

Education and qualification routes

• Degree / qualification: Master’s Degree or higher in a quantitative field (Statistics, Computer Science,Engineering, Mathematics)

Preferred experience and additional strengths

• 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: enterprise automation platforms, customer experience systems, workflow automation, or AI-powered business process automation
• Experience: observability and tracing for LLM or agent systems
• Experience optimizing agent systems for latency, cost, reliability, safety, and task success
• Experience: vector databases, search infrastructure, model routing, caching, ormulti-model architectures

Location, schedule and contract conditions

For this overseas or regional listing, confirm work authorisation and any location restrictions with the employer. The location does not establish visa sponsorship or permission to work remotely from India.

• Work arrangement: Remote
• Advertised location: Toronto/North America (Remote)

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Staff Agentic AI Engineer - Marketing vacancy is not an offer for reference 60fa8c9b-e031-476e-a9d3-011966c4b495; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Netomi

Applications follow the official employer page linked below.

Open the official Netomi posting below and match the title and reference 60fa8c9b-e031-476e-a9d3-011966c4b495.

Compare your qualifications and experience with the essential conditions for the Toronto/North America (Remote) position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• AWS
• AI/ML
• LLMs
• AI
• marketing
• machine learning
• business processes