Agentic AI Engineer

Role Overview: XenonStack is a rapidly growing Data and AI Foundry specializing in Agentic Systems, providing real-time, intelligent business insights. As an Agentic AI Engineer at XenonStack, you will be tasked with designing, developing, and deploying intelligent AI agents capable of autonomous reasoning, planning, and action within enterprise workflows. This role will require expertise in LLM-powered applications, multi-agent architectures, and building tool-augmented AI systems to drive measurable business impact through innovation. Key Responsibilities: - Agentic System Development: - Design, build, and optimize autonomous AI agents using frameworks like LangChain, LangGraph, CrewAI, AutoGen, or similar. - Implement multi-agent collaboration workflows with shared memory, context management, and task orchestration. - LLM Integration & Reasoning: - Fine-tune and integrate LLMs (OpenAI, Anthropic, LLaMA, Mistral, etc.) for reasoning, planning, and decision-making tasks. - Develop tool-using agents capable of interfacing with APIs, databases, and third-party services. - System Architecture & Orchestration: - Utilize Model Context Protocol (MCP) and A2A (Agent-to-Agent) messaging for scalable orchestration. - Implement hybrid reasoning + retrieval pipelines (vector + knowledge graph). - Performance & Safety: - Optimize inference performance and reduce operational costs on platforms like NexaStack. - Implement guardrails, monitoring, and evaluation to ensure safe and reliable agent behavior. - Collaboration & Deployment: - Collaborate closely with product, platform, and data engineering teams to integrate agents into enterprise systems. - Deploy AI agents to cloud, edge, or on-device environments as required. Qualifications Required: - Technical Skills: - Bachelors or Masters in Computer Science, AI/ML, or related field. - 2-4 years of experience in AI/ML engineering, with direct experience in LLM and agentic AI development. - Proficiency in Python and libraries/frameworks such as LangChain, LangGraph, CrewAI, AutoGen. - Experience with RAG pipelines, vector databases (Pinecone, Weaviate, Milvus), and knowledge graphs. - Understanding of distributed systems, API integration, and cloud-native architectures. - Familiarity with observability and monitoring for AI systems. - Professional Attributes: - Strong problem-solving and analytical thinking. - Ability to translate business workflows into autonomous AI solutions. - Excellent communication and collaboration skills. - Passion for exploring emerging AI capabilities and best practices.,