Applied AI Engineer Remote-Hermes Agent

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. Agent Architecture & Development: Framework: Hermes Agent Collaboration: orchestrator-workers, debate, hierarchical swarms Memory: short/long-term + episodic via vector DBs & semantic caching Reasoning & Planning: Techniques: ReAct, CoT, ToT, Plan-and-Solve Dynamic planning, error recovery, replanning from feedback Tool use: function calling, API grounding (DBs, APIs, RAG, UI automation) Production & Evaluation: Eval: agentic evals for task completion, efficiency, safety (not just lexical) Observability: tracing/logging (LangSmith, Arize, W&B) Optimize: latency, token cost, reliability Integration & Tooling: Connect: CRMs, DBs, Slack, browsers, REST APIs, code interpreters Custom tools + sandboxed envs for safe code/shell execution 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 5 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.