Agentic AI Engineer Intern

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 247 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
Were a fast-growing technology company building AI-powered automations and Agentic AI systems to run leaner and ship faster. Were 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 Youll 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 Youll Work With
Youll get hands-on experience with a modern Agentic AI stack:
 LLM APIs: OpenAI Claude Gemini
 Agent Frameworks: LangGraph LangChain CrewAI
 Automation Platforms: n8n  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 Were Looking For
 Genuine interest in Generative AI Agentic AI and automation  youve 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.