AI Interaction Engineer

Responsibilities

• Design, test, and refine prompt engineering strategies to drive optimal agent behavior.
• Build context orchestration pipelines integrating RAG, knowledge graphs, APIs, and layered memory.
• Optimize token usage and context allocation for multi-turn and multi-agent workflows.
• Develop reusable interaction templates and context blueprints for engineering teams.
• Implement safety, compliance, tone, and brand guardrails within agent workflows.
• Conduct A/B testing on prompt and context variations to measure accuracy, latency, and cost.
• Use execution traces and automated evaluation to continuously improve agent responses.

Requirements

• 2–4 years of experience in AI/ML engineering, NLP, or enterprise software development.
• Strong understanding of LLM architectures, prompt engineering, and context window limits.
• Hands-on experience with RAG pipelines, vector databases, and knowledge graph integration.
• Proficiency in Python and frameworks such as LangChain, LangGraph, or LlamaIndex.
• Ability to translate complex business objectives into structured AI interactions.

Preferred Qualifications

• Experience with multi-agent orchestration frameworks (MCP, A2A messaging, AgentBridge).
• Knowledge of reinforcement learning techniques like RLHF or RLAIF.
• Exposure to edge AI deployment and quantized inference.
• Domain knowledge in BFSI, GRC, SOC, or FinOps.

About the Company

Xenonstack is a fast-growing Data and AI Foundry for Agentic Systems. We enable organizations to gain real-time, intelligent business insights through our specialized platforms, including Akira AI, Vision AI, and NexaStack. Our mission is to accelerate the world’s transition to AI + Human Intelligence by reimagining how enterprises adopt and scale agentic AI.