Generative AI Internship in Nagpur (Online + Offline)
Applied AI engineering internship building production RAG pipelines, fine-tuning open-weights models, implementing guardrails, and deploying LLM applications.
This is a work-experience program, not a class. The Generative AI Internship at Skilleze Technologies in Nagpur gives candidates practical engineering experience building, evaluating, and deploying production LLM applications under senior mentorship. Interns implement Retrieval-Augmented Generation (RAG) systems, vector database pipelines, and agentic workflows with verifiable GitHub code proof.
Eligibility & Who Can Apply
Explicit candidate eligibility criteria for students, graduates, and working professionals.
2nd, 3rd, or final year students (B.E., B.Tech, BCA, MCA, M.Tech, M.Sc CS/IT/Data Science)
Computer Science, IT, AI & Data Science, Electronics, or related computational engineering fields
Graduates and professionals with proven Python proficiency and understanding of basic machine learning concepts
Software developers, data scientists, and backend engineers seeking production Generative AI implementation credentials
Internship Specifications At-a-Glance
Key operational parameters, batch structures, and documentation provided.
| Program Duration | 2 to 3 Months (Track dependent) |
|---|---|
| Weekly Commitment | 20–25 hours per week (Flexible evening/weekend slots available) |
| Work Mode | Offline at Nagpur Office / Hybrid / Remote live |
| Reporting Expectations | Daily async Slack standups, weekly pipeline walkthrough and PR review calls |
| Cohort Start Dates | 1st and 15th of every month (Cohort capacity capped at 15 interns) |
| Documents Provided |
|
Applied AI Engineering Sprints & Production Architectures
Interns operate as applied AI engineers building real-world generative systems. You will implement enterprise RAG pipelines, configure vector index chunking strategies, set up LLM evaluation frameworks (RAGAS), and integrate guardrails to prevent hallucinations.
Concrete Project Briefs Shipped
Enterprise Document Intelligence & Multimodal RAG Engine
Architect an enterprise search assistant capable of ingesting complex PDF documents, technical schematics, and tabular reports with source citation grounding.
Customer Support LLM Assistant with Guardrails & Evaluation
Engineer a contextual support bot equipped with NeMo Guardrails, automated fallback logic, conversation memory, and RAGAS quality evaluation.
Sprints are structured around LLM system milestones: prompt engineering, vector database indexing, context window optimization, reranking, and CI evaluation metrics.
Strict branch-and-PR model: feature branches, prompt versioning in code, automated unit tests for API endpoints, and PR templates requiring evaluation benchmark scores.
Senior AI engineers review every PR within 48 hours, inspecting token usage efficiency, context retrieval precision, latency profiles, and exception handling.
Daily 3-question async standup (what pipelines you tested, current latency/accuracy results, blockers) plus weekly architecture design reviews.
Engineering Definition of Done (DoD)
Phase-by-Phase Deliverable Milestone Plan
Gate-reviewed deliverables submitted at each milestone. This is a deliverable roadmap, not a classroom topic list.
LLM Foundations, API Integration & Embedding Ingestion
Connecting to state-of-the-art model APIs, implementing semantic chunking, and populating vector databases.
Senior mentor review of chunking strategies, embedding dimensionality, and index design.
Advanced RAG, Hybrid Search & Context Reranking
Building hybrid BM25 + dense vector retrieval, cross-encoder reranking, and conversational memory buffering.
Code review evaluating retrieval recall, reranking latency, and hallucination guardrails.
System Evaluation, Guardrails & Production Deployment
Automated evaluation using RAGAS, input/output guardrail policies, and containerized FastAPI cloud deployment.
Production readiness audit testing edge cases, safety filters, and response latency.
Career Accelerator, Portfolio Showcase & Technical Viva
Publishing AI project case studies, compiling ATS resume for Generative AI / LLM Engineer roles, and mock interviews.
Final technical evaluation and issuance of verified experience certificate.
Mentorship, Code Reviews & Performance Evaluation
Practicing AI Engineers and Machine Learning Tech Leads with hands-on production experience deploying LLM architectures.
Continuous GitHub PR reviews (within 48 hours) plus weekly 1-on-1 architecture design clinics and prompt engineering critiques.
- Retrieval pipeline architecture, embedding selection, and vector search efficiency
- Code quality, modularity, and API design in Python
- Rigorous evaluation methodology (hallucination mitigation, latency benchmarks)
- Problem-solving independence and technical curiosity
Interns who engineer robust LLM systems, achieve top evaluation benchmark scores, and document clean reproducible repositories receive executive letters of recommendation and priority referral for AI roles.
Deliverables You Graduate With
Everything you walk away with upon successful completion of your internship track.
2 Production Generative AI Applications
End-to-end LLM applications with hybrid vector search, context reranking, streaming endpoints, and interactive frontends.
Public GitHub AI Portfolio
Documented code repositories demonstrating modular Python architecture, vector DB configurations, and evaluation scripts.
RAGAS Benchmark & Evaluation Reports
Documented test reports proving retrieval faithfulness, answer relevancy, and latency metrics across diverse test sets.
ATS-Optimized GenAI Engineer Resume
Specialized resume highlighting production LLM implementations, vector architectures, and quantifiable accuracy gains.
AI System Design & Mock Interviews
Technical mock interviews covering context window trade-offs, vector search scaling, and LLM orchestration challenges.
Verifiable Experience Certificate
Official company experience certificate with permanent QR verification linking to your public GitHub contributions.
For College Students: Industrial Training & University Credit
Complies with academic requirements for industrial training, summer projects, and final-semester major project credits.
We issue company offer letters, project synopses, bi-weekly guide review forms, and comprehensive final project report books.
Flexible sprint scheduling built to accommodate college exams, university vivas, and campus placement drives.
Structured to fulfill 6-week to 6-month industrial internship guidelines for RTMNU, YCCE, VNIT, RCOEM, and GHRCE <<CONFIRM: which university formats you support — no false claims of affiliation>>.
For Working Professionals: Evening/Weekend Project Sprints
Async sprint tickets and flexible review calls allow employed software engineers to gain hands-on GenAI experience without leaving their current roles.
Replaces basic online tutorial certificates with actual production LLM commits, vector database pipelines, and verifiable GitHub proof.
Rather than watching tutorials on prompt engineering, you write production code, evaluate retrieval precision, and deploy live cloud AI services.
Track Comparison: 45 Days vs 2 vs 3 vs 6 Months
Compare duration tracks by project deliverables, mentorship hours, and Career Accelerator inclusion.
| Track Feature | 45-Day Track | 2-Month Track | 3-Month Accelerator | 6-Month Incubation |
|---|---|---|---|---|
| Shipped Projects | 1 Production Project | 2 Production Projects | 3 Production Projects | 3 Enterprise Systems |
| Engineering Depth | Fast-Track Delivery | Modular Feature + Capstone | Multi-Tier Cloud Services | Distributed Architecture & Microservices |
| PR Mentorship Hours | 15+ Dedicated Hours | 30+ Dedicated Hours | 45+ Dedicated Hours | 80+ Dedicated Hours |
| Career Accelerator | Resume Entry Only | Resume + Mock Viva | Full Module Included | Full Module + System Design |
| Best-Fit Persona | Vacation training students | 2nd/3rd year engineers | Final years & career switchers | Full-semester college capstone |
| Explore Track | 45-Day Details | 2-Month Details | 3-Month Details | 6-Month Details |
Verifiable Proof: Projects, Certificate & Intern Feedback
All credentials link to tamper-proof QR codes and public GitHub contributions.
Official Generative AI Engineering Experience Certificate
Issued by Skilleze Technologies (Industrial Work Experience Vertical)
- Permanent QR verification link
- Direct link to candidate’s merged GitHub repositories and PRs
- Detailed breakdown of LLM architectures, models, and sprint deliverables
- AI Engineering Lead seal and authorized signature
“<<PLACEHOLDER: intern testimonials — Implementing RAGAS evaluation and vector reranking in a real team environment gave me the exact skills demanded in AI startup interviews.>>”
“<<PLACEHOLDER: intern testimonials — The mentor reviews on chunking strategies and token optimization taught me engineering nuances you never see in online courses.>>”
4-Step Application & Screening Process
Capped at 15 interns per cohort to guarantee direct, line-by-line engineering reviews and dedicated mentor feedback.
Online Application Submission
Submit your profile, target duration track, Python background, and GitHub profile.
Python & LLM Logic Screening
Complete a practical screening task demonstrating basic API calling, JSON parsing, and Python data structures.
Technical Discussion & Track Alignment
15-minute alignment call with an AI mentor to review project briefs, tools, and expectations.
Offer Letter & Sprint Kickoff
Receive your official Offer Letter, environment setup guide, API keys, and attend sprint kickoff.
Frequently Asked Questions About Internships
Plain answers to the most common questions from students and working professionals.
Will I get an official offer letter for this GenAI internship?
Yes. Every selected intern receives an official Selection and Offer Letter detailing the AI engineering role, track duration, mentor assignment, and project scope before cohort onboarding.
Is this internship valid for college academic credit?
Yes. Our documentation includes standard offer letters, periodic evaluation reports, and final completion certificates compliant with university industrial training mandates.
Can I do it in my final semester for my major project?
Yes. Final-year students regularly complete their capstone project with us, building cutting-edge Generative AI applications that satisfy college project evaluation panels.
Is it work from home or office in Nagpur?
We offer both in-person desk allocation at our Nagpur development facility and fully live remote/hybrid formats with daily async standups and weekly screen-share code reviews.
Do I need a high-end GPU machine to participate?
No. Most production LLM workflows rely on cloud API providers, managed vector databases, and lightweight local Python environments, all runnable on standard development laptops.
How do I know if I am eligible?
Anyone with comfortable Python programming fundamentals and basic understanding of machine learning or APIs is eligible to apply for technical screening.
What happens after I apply?
Our technical team reviews your application within 24 hours. Eligible candidates receive a practical Python screening challenge followed by a track alignment consultation.
How do I get details about enrolment and commercial terms?
The internship delivers real production project execution, senior mentor PR reviews, verifiable GitHub proof, and official experience credentials. Commercial terms, track allocations, and enrolment schedules are shared one-to-one upon enquiry — connect with an advisor on WhatsApp to discuss current cohort availability.
What frameworks will I actually build with?
You will work with LangChain, LlamaIndex, LangGraph, FastAPI, vector databases (Qdrant, Chroma, Pinecone), and evaluation suites like RAGAS and NeMo Guardrails.
Will my code and projects be public for my portfolio?
Yes. All internship capstone projects are maintained in public GitHub repositories with MIT or permissive licenses so you can showcase verifiable PRs to employers.
Ready to Build Production Software?
Join our next cohort, receive senior PR code reviews, and graduate with verifiable GitHub proof for recruiters.
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Courses vs Internships Decision Framework
Side-by-side comparison to help tech students choose between curriculum mastery and work experience.
Nagpur College Student Career Accelerator Policy
Semester-break schedules, university NOC support, and hands-on project preparation for campus drives.