Applied AI Engineer

Role overview

Responsibilities

• check_circle Design and implement autonomous agent systems using frameworks using Hermes Agent.
• check_circle Build multi-agent collaboration patterns (e.g., orchestrator-workers, debate, hierarchical swarms).
• check_circle Implement agentic memory systems (short-term, long-term, and episodic memory) using vector databases and semantic caching.
• check_circle Integrate advanced reasoning techniques: ReAct, Chain-of-Thought (CoT), Tree-of-Thoughts (ToT), and Plan-and-Solve.
• check_circle Develop agents capable of dynamic planning, error recovery, and replanning based on environmental feedback.
• check_circle Implement tool use (function calling) and API grounding for actions like database queries, API calls, RAG retrieval, and UI automation.
• check_circle Build robust evaluation frameworks (agentic eval) to test for task completion, efficiency, and safety—not just lexical similarity.
• check_circle Instrument agents with tracing, observability, and logging (e.g., LangSmith, Arize, Weights & Biases).
• check_circle Optimize for latency, cost (token usage), and reliability in production.
• check_circle Connect agents to internal and external systems: CRMs, databases, Slack, browsers, REST APIs, and code interpreters.
• check_circle Develop custom tools and sandboxed environments for agents to execute code or shell commands safely.

Basic qualifications

• 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
• 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.
• The role requires a production experience with MCP and sufficient experience using Hermes Agent framework.