Applied AI Engineer

Description

**About the Role:** We are seeking a forward\-thinking Agentic AI Engineer to design, build, and orchestrate autonomous AI agents capable of reasoning, planning, and executing complex workflows. Unlike traditional LLM\-based chatbots, our agents interact with dynamic environments, use tools, collaborate with other agents, and operate with minimal human intervention. **Key Responsibilities:** Agent Architecture \& Development: * Design and implement autonomous agent systems using frameworks using Hermes Agent. * Build multi\-agent collaboration patterns (e.g., orchestrator\-workers, debate, hierarchical swarms). * Implement agentic memory systems (short\-term, long\-term, and episodic memory) using vector databases and semantic caching. Reasoning \& Planning: * Integrate advanced reasoning techniques: ReAct, Chain\-of\-Thought (CoT), Tree\-of\-Thoughts (ToT), and Plan\-and\-Solve. * Develop agents capable of dynamic planning, error recovery, and replanning based on environmental feedback. * Implement tool use (function calling) and API grounding for actions like database queries, API calls, RAG retrieval, and UI automation. Production \& Evaluation: * Build robust evaluation frameworks (agentic eval) to test for task completion, efficiency, and safety—not just lexical similarity. * Instrument agents with tracing, observability, and logging (e.g., LangSmith, Arize, Weights \& Biases). * Optimize for latency, cost (token usage), and reliability in production. Integration \& Tooling: * Connect agents to internal and external systems: CRMs, databases, Slack, browsers, REST APIs, and code interpreters. * Develop custom tools and sandboxed environments for agents to execute code or shell commands safely. **Required Qualifications:** Technical Skills: * **Programming**: Expert in Python * Strong understanding of prompt engineering, few\-shot learning, and structured output generation (JSON mode, grammars). * **Reasoning Patterns**: Proven experience implementing agentic patterns (ReAct, Reflexion, Toolformer) in production or complex prototypes. * **Memory \& Retrieval:** Experience with vector databases (Pinecone, Weaviate, Qdrant) and RAG optimization (hybrid search, reranking). * **Orchestration**: Familiarity with workflow engines (Temporal, Prefect, Airflow) for human\-in\-the\-loop and durable execution. * **Observability**: Experience monitoring LLM applications (prompt traces, token usage, drift). * **Model Context Protocol:** Built agents that use MCP for multi\-step research, code analysis, or data engineering tasks. * **Agentic Framework** : Practical experience with Hermes Agent Education \& Experience: * Bachelor’s degree in Computer Science, Software Engineering, AI, or related discipline * 3 years in software engineering / ML engineering. * Experience building production\-grade agentic systems (not just demos or chatbots). * Strong understanding of LLM limitations: hallucinations, jailbreaks, prompt injection, and failure modes. * Good understanding of MCP discovery patterns and context negotiation. * Strong knowledge of context management in LLM applications: prompt caching, sliding window, semantic retrieval, MCP resource lifecycle. Job Type: Full\-time Pay: 1\.00€ \- 2\.00€ per year Application Question(s): * The role requires a production experience with MCP and sufficient experience using Hermes Agent framework. Briefly describe your practical experience on both. Work Location: Remote