Staff Agentic AI Engineer - Marketing
New
N
NetomiEnterprise AI
Toronto/North America (Remote)Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 5-7+ years
- Required Skills
- AWSPythonMachine LearningNLPLangChain
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
Responsibilities
- Design, build, and improve production-grade AI agentic systems that optimize web-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
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