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.

• 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.

• 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.

• 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.

• 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).

• 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.