AI Engineer-Hermes Agent

Compensation: No equity

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
• Strong background on Spec-Driven Development ( SDD ) methodology
• Practical experience installing, configuring, and operating Hermes Agent (the self-improving AI agent framework from Nous Research)
• Experience building production-grade agentic systems (not just demos or chatbots).
• Must be well versed with any of the following Method:

1. BMAD ( Breakthrough Method for Agile AI-Driven Development )
2. Github Spec Kit
3. OpenSec

• 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.