Are you passionate about artificial intelligence and eager to work with cutting-edge LLMs and Gen-AI technologies? The Full Stack AI/Gen-AI Developer Internship at CloudRedux Pune offers an excellent opportunity for fresh graduates and early-career professionals (0-2 years) to gain hands-on experience with Retrieval Augmented Generation (RAG), conversational AI, and LLM-driven solutions.
CloudRedux is an AI Tech organization breaking boundaries and embracing latest technologies to create comprehensive solutions for modern brands. This paid internship provides mentorship from experienced engineers, opportunities to work on real AI-driven products and services, and potential conversion to full-time employment based on performance. You’ll develop RAG workflows, build AI frameworks, and contribute to innovative Gen-AI solutions.
🚀 About the AI Developer Internship at CloudRedux
This internship focuses on developing scalable LLM-driven systems including RAG workflows, conversational AI pipelines, and multi-modal agent frameworks. You’ll work alongside experienced engineers on real projects, contributing to both product development and service delivery while learning cutting-edge AI technologies. The role combines hands-on development with structured learning and mentorship.
📊 Internship Details
| Detail | Information |
|---|---|
| Company | CloudRedux |
| Position | Full Stack AI/Gen-AI Developer (Internship) |
| Location | Pune, Maharashtra, India |
| Job Type | Paid Internship |
| Experience | 0-2 Years |
| Work Focus | AI/ML, LLMs, Gen-AI, RAG, Conversational AI |
| Conversion Potential | Full-time based on performance |
About CloudRedux
Company Vision and Culture
CloudRedux is more than just a company:
- Passion-Driven: Breathe life into work and love what they do
- Impact-Focused: Creating solutions that drive change and truly matter
- AI Tech Organization: Breaking boundaries with latest technologies
- Innovation-Centric: Embracing cutting-edge AI and Gen-AI solutions
Primary Internship Responsibilities
RAG Workflow Development and Deployment
- Assist in developing scalable LLM-driven Retrieval Augmented Generation (RAG) workflows
- Help deploy RAG systems in cloud-based environments (AWS, Azure, GCP)
- Build document processing pipelines for RAG applications
- Implement vector databases and embedding systems for retrieval
- Optimize retrieval mechanisms for accuracy and performance
- Work on chunk strategies and indexing for efficient retrieval
- Integrate RAG systems with various LLM providers (OpenAI, Anthropic, etc.)
Agent Workflows and Framework Development
- Contribute to design and implementation of domain-specific agent workflows
- Build frameworks supporting multi-turn conversations with context management
- Develop multi-modal systems handling text, images, and other data types
- Create multi-user conversation systems with state management
- Implement agent memory and context preservation mechanisms
- Build tool-use capabilities for agents to interact with external systems
- Design agent orchestration patterns for complex workflows
Conversational AI Systems Development
- Assist in developing systems improving AI conversation understanding
- Work on clarification mechanisms for handling ambiguous queries
- Build systems for resolving misunderstandings in AI conversations
- Implement dialogue management and turn-taking mechanisms
- Create context-aware response generation systems
- Develop intent recognition and entity extraction pipelines
- Optimize conversational flows for better user experience
Evaluation and Performance Analysis
- Help evaluate RAG pipelines analyzing accuracy and relevance
- Assess conversational AI performance under senior guidance
- Measure system metrics including latency, throughput, and quality
- Conduct A/B testing of different approaches
- Analyze failure cases and identify improvement areas
- Document evaluation results and insights
- Contribute to benchmarking against industry standards
System Improvement and Optimization
- Participate in identifying system limitations and bottlenecks
- Recommend improvements in collaboration with experienced engineers
- Optimize prompts for better LLM responses
- Enhance retrieval quality through better indexing
- Improve system performance and reduce latency
- Contribute ideas for feature enhancements
- Learn from code reviews and incorporate feedback
Development and Collaboration
- Engage in code development following best practices
- Participate in design reviews learning system architecture
- Contribute to testing processes including unit and integration tests
- Receive mentorship from senior engineers
- Collaborate with cross-functional teams on projects
- Document code and technical implementations
- Present work and learnings to team members
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📋 Required Qualifications and Skills
Experience Requirements
Required: 0-2 Years of programming experience
Acceptable Backgrounds:
- Fresh graduates with strong technical skills
- 1-2 years professional experience in programming
- Internships in AI/ML or software development
- Significant academic projects in relevant areas
Must-Have Technical Skills
| Skill Category | Requirements |
|---|---|
| Python | 1-2 years experience, preferably Python (strong skills for freshers) |
| LLMs | Basic understanding of Large Language Models and applications |
| NLP | Some exposure to Natural Language Processing |
| Conversational AI | Familiarity with dialogue systems |
| Version Control | Knowledge of Git is beneficial |
| IDE Tools | Familiarity with VS Code, PyCharm, or Jupyter Notebooks |
Educational Background
Preferred:
- Bachelor’s degree in Computer Science, Engineering, AI/ML, or related field
- Strong academic foundation in programming and algorithms
- Coursework in machine learning, NLP, or AI
- Projects demonstrating technical capabilities
💰 Internship Compensation and Benefits
Paid Internship
| Component | Details |
|---|---|
| Type | Paid Internship |
| Stipend | Competitive (specific amount not disclosed) |
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Application Tips
- Highlight any Gen-AI or LLM projects you’ve built
- Showcase Python proficiency through concrete examples
- Emphasize understanding of RAG systems and conversational AI
- Include GitHub profile with AI/ML projects
- Demonstrate learning agility and passion for AI
- Express genuine interest in LLMs and Gen-AI applications
- Prepare to discuss recent developments in AI (GPT-4, Claude, Gemini)
- Show problem-solving approach through project examples
- Mention any experience with LangChain, vector databases, or APIs
- Research CloudRedux and prepare thoughtful questions
- Demonstrate collaborative mindset through team projects
- Express eagerness to learn and grow in AI field
Important Notes
🎓 Fresh Graduates Welcome: Strong skills more important than years of experience
🤖 Gen-AI Focus: Heavy emphasis on LLMs, RAG, and conversational AI
📚 Learning Emphasis: Mentorship and skill development are core to the role
💼 Full-Time Path: Performance-based conversion to permanent position
👔 Relaxed Environment: Not formal dress code, collaborative culture
📍 Pune Location: Based in Pune’s growing tech ecosystem
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