AI Infrastructure & RAG Engineering Intern

AI Infrastructure & RAG Engineering Intern
Company: Autokryx Technologies Private Limited
Product: INSIDE — The Intelligence Layer for Higher Education
Employment Type: Internship
Work Mode: Hybrid
Stipend: Up to ₹5,000/month, performance-based
About the Role
We are looking for an AI Infrastructure & RAG Engineering Intern to work closely with our engineering team on backend infrastructure, data systems, retrieval-augmented generation, semantic search, and AI-powered applications.
This is a hands-on engineering role involving real product infrastructure, databases, APIs, cloud systems, and LLM pipelines.
What You’ll Work On
Design, develop, and maintain PostgreSQL databases
Build database synchronization and data ingestion pipelines
Develop REST APIs and backend services
Work with Node.js / TypeScript / Deno
Build and optimize RAG pipelines
Implement embeddings, vector search, and semantic retrieval
Work with pgvector and PostgreSQL-based vector infrastructure
Develop prompt engineering and LLM workflows
Build real-time data aggregation and processing systems
Integrate and manage AWS cloud infrastructure
Improve retrieval quality, latency, reliability, and scalability of AI systems
Debug, test, document, and optimize production-oriented systems
Required Skills
Strong understanding of PostgreSQL and relational databases
Familiarity with backend development and REST APIs
Working knowledge of Node.js, TypeScript, or Python
Understanding of Git and GitHub
Basic understanding of AI/LLM applications
Understanding of RAG, embeddings, or semantic search
Strong problem-solving and debugging ability
Good to Have
Experience with pgvector
Experience building RAG applications
AWS experience
Deno experience
Experience with vector databases
Experience with real-time systems / WebSockets
Experience with LLM APIs
Knowledge of data pipelines and asynchronous processing
Understanding of database indexing and query optimization
Who Should Apply
We are looking for someone who enjoys building systems, not just following tutorials.
You do not need to know every technology listed above. Strong backend fundamentals combined with genuine hands-on experience in AI/LLM projects are highly valued.
Candidates with personal projects, GitHub repositories, deployed applications, or previous experience building RAG/AI systems are encouraged to apply.
Stipend
Up to ₹5,000 per month — performance-based.
Performance will be evaluated based on technical contribution, quality of implementation, ownership, consistency, problem-solving, and ability to deliver assigned engineering work.
What You’ll Gain
Hands-on experience building AI infrastructure
Practical exposure to RAG and LLM systems
Experience with PostgreSQL, pgvector, APIs, and AWS
Exposure to production-oriented engineering practices
Opportunity to work on a live AI product
Direct collaboration with the core engineering team
Application
Please apply with your resume and GitHub/portfolio.
Candidates may be asked to complete a technical assessment or technical discussion as part of the selection process.

Work arrangement
Hybrid