About the role
As an Agentic AI Engineer at WPMaven, you'll build the operators that run a business: agents that understand what the business is, plan a week of work, and carry it out to the point where the owner only has to approve it. You'll work on the agent architecture behind the whole product.
Responsibilities
- Train and configure agents that run a real business, aligned with the owner's goals, constraints and budget.
- Build the workflows, toolchains and decision logic behind the work itself: products, store, content, publishing and the weekly report.
- Adapt reasoning, planning and state management so a week of work stays coherent from plan to published.
- Work with people who have run real businesses to define task flows and validate what the agents produce.
- Fine-tune models and configure tool integrations to support effective interaction, tool use, and decision-making in real-world scenarios.
- Monitor agent performance, identify misalignments with user expectations, and iteratively improve workflows and configurations.
- Support evaluation, simulation, and continuous learning pipelines to refine agent behavior based on feedback and performance data.
- Document configurations, training protocols, and best practices for scalable deployment.
Requirements
Experience
- 5+ years in AI system training, workflow configuration, or intelligent system customization, with a focus on domain adaptation.
- Experience working with AI/ML tools in applied settings.
Technical skills
- Strong proficiency in Python and experience with AI/ML frameworks (e.g., PyTorch, TensorFlow).
- Familiarity with agentic AI workflows, including planning, tool use, and multi-step task execution.
- Hands-on experience configuring AI tools, models, and pipelines for use in specialized environments.
- Ability to interpret domain-specific requirements and translate them into actionable agent behaviors.
Preferred knowledge
- Exposure to multi-agent systems, LLM orchestration frameworks (e.g., LangChain, AutoGen), or intelligent automation platforms.
- Experience working with structured knowledge bases, ontologies, or knowledge graph integrations.
- Familiarity with simulation environments or digital twins for AI testing and training.
- Experience with systems that publish to third-party platforms is a plus.