Agentic AI Engineer Intern

About Xiarch Bharat Infrastructure & Security Operations Xiarch Bharat is a CERT-In empanelled cybersecurity services company with over 20 years of experience. We specialize in 24×7 SOC monitoring, Vulnerability Assessment & Penetration Testing (VAPT), GRC, DPDP compliance, ISO audits, and Zero Trust networking solutions (pfSense/FortiGate). We serve 4000+ clients across India and are rapidly scaling our infrastructure and product capabilities with AI-powered security solutions. About the Role We’re a fast-growing technology company building AI-powered automations and Agentic AI systems to run leaner and ship faster. We’re looking for sharp, self-driven freshers and interns who genuinely enjoy building with AI — people who would rather create an intelligent system that handles a task 100 times than do it manually once. This is a pure AI engineering and automation role. You will work directly with the founder and technical teams to identify repetitive and knowledge-intensive processes and turn them into reliable, AI-assisted workflows. Your job will be to design, build, integrate, test, and deploy AI agents and automation pipelines. You will start building and shipping real AI systems from week one. What You’ll Do • Design and build AI agents and multi-agent workflows for internal business automation. • Build agentic workflows using LangGraph, LangChain, CrewAI, or similar frameworks. • Integrate LLM APIs such as OpenAI and Claude into production workflows. • Implement tool calling, function calling, structured outputs, agent routing, and workflow orchestration. • Build RAG-based knowledge assistants using documents, internal knowledge bases, and structured data. • Work with embeddings, vector databases, hybrid search, and retrieval pipelines. • Connect agents with internal systems using REST APIs, webhooks, databases, and external tools. • Build AI-powered research, document processing, report-generation, data extraction, and decision-support workflows. • Develop Python services and scripts to support AI agents and automation pipelines. • Work with structured and unstructured data including PDFs, spreadsheets, emails, reports, logs, and databases. • Implement agent memory, state management, human-in-the-loop workflows, and error handling. • Test, debug, document, and continuously improve AI workflows for reliability and scalability. • Work with the team to identify new opportunities where AI agents can replace repetitive manual processes. • Deploy and maintain AI applications and automation pipelines. Tools You’ll Work With You’ll get hands-on experience with a modern Agentic AI stack: • LLM APIs: OpenAI, Claude, Gemini • Agent Frameworks: LangGraph, LangChain, CrewAI • Automation Platforms: n8n, Make.com, Zapier • AI Technologies: RAG, Embeddings, Vector Search, Tool Calling, Function Calling, MCP • Languages & Frameworks: Python, FastAPI • Data & Integrations: REST APIs, Webhooks, PostgreSQL, Vector Databases • Vector Databases: Qdrant, FAISS, pgvector • Development Tools: GitHub, Docker, Linux • Productivity & Knowledge: Google Workspace, Notion What We’re Looking For • Genuine interest in Generative AI, Agentic AI, and automation — you’ve already experimented with ChatGPT/Claude, AI agents, n8n/Make/Zapier, or similar tools on your own. • Basic coding ability, preferably Python — enough to write scripts, call APIs, process data, and debug issues. • Understanding of LLMs, prompting, APIs, and basic RAG concepts is preferred. • Logical, structured thinking — you can break a messy manual process into clear, automatable steps. • Interest in building AI agents that actually perform tasks, rather than only generating text. • Strong ownership and curiosity — you figure things out, ask good questions, and take initiative without waiting to be told what to do. • Comfortable reading technical documentation and learning new AI tools quickly. • Good written English for documentation, prompts, and technical communication. Nice to Have • Experience building a personal AI agent or RAG project. • Experience with LangGraph, LangChain, CrewAI, or similar frameworks. • Experience with Qdrant, FAISS, Pinecone, or pgvector. • Experience with MCP or tool-calling systems. • Familiarity with FastAPI, Docker, Git, or cloud deployment. • GitHub portfolio demonstrating AI/automation projects.