Website:
venxr.tech
Job details:
About Venxr
Venxr is building AdsGPT, a conversational AI platform that enables brands to plan, execute, and optimise advertising campaigns through natural language.
We're looking for a curious and hands-on Generative AI Engineering Intern who wants to go beyond basic prompt engineering and learn how to build real-world applications powered by Large Language Models (LLMs).
If you've experimented with LangChain, LangGraph, MCP, AI agents, or memory systems and enjoy figuring out how things work under the hood, we'd love to hear from you.
What You'll Work On
- Build and improve LLM-powered features for AdsGPT.
- Develop AI workflows that help users plan campaigns, retrieve information, and interact with advertising tools.
- Experiment with AI agents, tool calling, and multi-step reasoning.
- Implement context engineering and memory management to improve the quality of AI responses.
- Integrate LLMs with APIs, databases, and external tools.
- Test AI-generated outputs, identify failure cases, and improve reliability.
- Collaborate with the product and engineering teams to turn ideas into working prototypes and product features.
Tech Stack
- AI/LLMs: Claude and other frontier models, LLM APIs.
- AI frameworks: LangChain, LangGraph, or equivalent orchestration frameworks.
- AI engineering: MCP, LLM orchestration, context engineering, memory management, and tool calling.
- Backend: Python, Django or FastAPI.
- Frontend: React.js.
You don't need to know every technology listed, but you should have a strong interest in AI engineering and practical experience with at least some of these tools.
What You'll Explore
We want you to understand how AI applications work beyond sending prompts to a model.
- LLM fundamentals: Tokens, context windows, structured outputs, latency, cost, and hallucinations.
- AI orchestration: Building workflows with LangChain, LangGraph, or similar frameworks.
- Context engineering: Managing what information the model receives, including retrieval, context selection, and context-window optimisation.
- Memory management: Maintaining conversational history, state, and relevant information across interactions without unbounded context growth.
- AI agents and tool calling: Connecting models to APIs, tools, and external systems.
- MCP: Understanding how models interact with external tools and services through the Model Context Protocol.
- Evaluation and debugging: Testing AI responses, identifying failure modes, and improving system reliability.
- RAG and retrieval: Working with external knowledge sources to generate more relevant responses.
What We're Looking For
- 0–1 year of experience in AI engineering, product development internship, or related projects.
- B.Tech in AI/ML or CSE.
- Good foundational knowledge of Python.
- Hands-on experience building at least one GenAI application, AI agent, chatbot, or LLM-powered workflow.
- Hands-on experience with LangChain, LangGraph, MCP, or equivalent AI frameworks, with an understanding of LLM orchestration, context engineering, memory management, and tool calling.
- Basic knowledge of REST APIs and databases.
- Strong problem-solving and debugging skills.
- Willingness to experiment, learn quickly, and explain your technical decisions.
We value practical implementation over certificates. Personal projects, hackathons, academic projects, and open-source contributions count. You should be able to explain what you built, how it works, and what you would improve.
Good to Have
- Experience with RAG, embeddings, vector databases, or semantic search.
- Exposure to multi-agent systems and workflow orchestration.
- Familiarity with LLM evaluation tools such as LangSmith, Langfuse, or Ragas.
- Basic React.js or frontend development experience.
- Familiarity with Git, Docker, cloud deployment, or CI/CD.
- Interest in Generative UI, AdTech, or MarTech.
How We Work
At Venxr, you'll work on real product challenges alongside the engineering and product teams. You'll have opportunities to experiment with new AI approaches, build prototypes, debug complex workflows, and contribute to features that can make their way into a live product.
We value curiosity, ownership, thoughtful problem-solving, collaboration, and a willingness to learn from failures.
We're looking for people who ask questions, take initiative, and enjoy turning ideas into working solutions.
Selection Process
- Technical screening: A 30-minute discussion about your projects, Python fundamentals, and GenAI concepts.
- Take-home assignment: Build a small LLM-powered workflow or agent. AI coding tools are welcome, but you must understand and explain your implementation.
- Technical interview: Walk us through your solution, design decisions, debugging approach, and potential improvements.
- Final round: Technical and managerial discussion.
How To Apply
Share your resume along with links to your GitHub, portfolio, or GenAI projects.
Note: This is a paid internship.Skills: django,ci/cd,langgraph,langchain,cloud infra,nosql,docker,context engineering,system design,rest apis,sql,adtech,mcp,python,memory management,react.js,llm orchestration,tool calling,debugging,generative ui
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