AI Engineer

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 • Agentic Framework : Practical experience with Hermes Agent • Model Context Protocol: Built agents that use MCP for multi-step research, code analysis, or data engineering tasks. • 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). 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.