Agentic AI Internship in Nagpur (Online + Offline)
Advanced engineering internship architecting autonomous multi-agent swarms, Model Context Protocol (MCP) integrations, human-in-the-loop workflows, and self-healing systems.
This is a work-experience program, not a class. The Agentic AI Internship at Skilleze Technologies in Nagpur gives candidates hands-on experience designing and deploying autonomous multi-agent systems under senior engineering mentorship. Interns build LangGraph state machines, implement Model Context Protocol (MCP) servers, and ship production agent swarms with verifiable GitHub code proof.
Eligibility & Who Can Apply
Explicit candidate eligibility criteria for students, graduates, and working professionals.
3rd or final year students (B.E., B.Tech, MCA, M.Tech, M.Sc CS/IT)
Computer Science, IT, Artificial Intelligence, Electronics, or related technical engineering streams
Technical graduates or professionals with demonstrated software engineering experience in Python or TypeScript
Software engineers and system architects seeking production expertise in autonomous agents and MCP tooling
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 multi-agent design walkthrough and code review calls |
| Cohort Start Dates | 1st and 15th of every month (Cohort capacity capped at 15 interns) |
| Documents Provided |
|
Autonomous Systems Architecture & Production Agent Workflows
Interns build production-grade autonomous agent systems capable of multi-step planning, tool use, reflection, and human-in-the-loop escalation. You will design state machines, build custom Model Context Protocol (MCP) servers, and evaluate multi-agent swarm reliability.
Concrete Project Briefs Shipped
Autonomous Code Refactoring & Security Audit Agent Swarm
Design a coordinated multi-agent system where specialized agents plan, review git diffs, generate test suites, and refactor security vulnerabilities in third-party repositories.
Enterprise Financial Research & Compliance Agent Network
Engineer a supervisor-worker agent network that orchestrates web search, SEC filing analysis, tabular synthesis, and automated compliance verification.
Sprints focus on agent architecture challenges: state graph design, memory persistence, tool definition contracts, loop termination policies, and error recovery strategies.
Strict branch-and-PR model: feature branches, schema definitions for tool inputs/outputs, automated integration tests for agent tools, and evaluation traces on every PR.
Senior AI architects review code within 48 hours, inspecting loop termination guarantees, context state boundaries, tool call reliability, and token spend caps.
Daily 3-question async standup (agent behaviors tested, tool bugs resolved, blockers) plus weekly live swarm architecture demonstrations.
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.
Agent Architecture, Tool Interfaces & State Design
Understanding agent loops (ReAct pattern), building custom tool interfaces, and setting up LangGraph state schemas.
Lead review of state isolation, tool validation schemas, and error catching.
Multi-Agent Coordination & Model Context Protocol (MCP)
Building supervisor/worker topologies, inter-agent message routing, and implementing custom MCP servers.
Architecture review evaluating delegation logic, context pollution prevention, and protocol compliance.
Memory Persistence, Human-in-the-Loop & Evaluation
Implementing long-term episodic memory, human approval pause/resume gates, and agent trajectory evaluations.
Production readiness audit inspecting self-healing behavior and cost control.
Career Accelerator, Portfolio Publishing & Exit Viva
Assembling GitHub agentic portfolio, tuning ATS resume for AI Systems / Agent Engineer roles, and mock technical viva.
Final evaluation by technical director and issuance of verified experience certificate.
Mentorship, Code Reviews & Performance Evaluation
Principal AI Engineers and Systems Architects with direct experience building production agent workflows and distributed systems.
Continuous GitHub PR reviews (within 48 hours) plus weekly 1-on-1 agent architecture reviews and live tracing sessions.
- Agent reliability, loop termination safety, and state graph design
- Clean tool abstraction and schema validation
- Handling of nondeterministic model behaviors and fallback planning
- Code quality, modularity, and comprehensive documentation
Interns who architect robust multi-agent systems, demonstrate exceptional problem-solving in handling model uncertainty, and publish clean open-source repos earn executive recommendation letters and recruitment referrals.
Deliverables You Graduate With
Everything you walk away with upon successful completion of your internship track.
2 Autonomous Multi-Agent Systems
Production agent swarms featuring LangGraph state machines, custom MCP tool integrations, and human approval gates.
Public GitHub Agentic Footprint
Documented repositories with architecture diagrams, tool schemas, state flow graphs, and evaluation reports.
Custom MCP Server Implementation
Standardized Model Context Protocol server exposing custom tools and resources to modern AI environments.
ATS-Formatted AI Systems Resume
Targeted resume highlighting multi-agent orchestration, state machine design, MCP protocols, and measurable efficiency gains.
Agentic System Design Mock Interviews
One-on-one technical mock viva focusing on agent failure modes, memory architectures, and scalable tool execution.
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 university industrial training, summer internship, and final-semester major project requirements.
We supply company offer letters, synopsis documents, guide evaluation reports, and formal project completion books.
Flexible sprint scheduling built around semester exams, viva examinations, and university schedules.
Structured to satisfy industrial training mandates across 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 task assignment and flexible weekend review sessions allow employed engineers to build agentic AI credentials without disrupting their current job.
Positions you ahead of the industry curve with verifiable multi-agent implementations and MCP expertise that few developers possess.
Instead of watching introductory agent demos, you engineer resilient state graphs, debug tool execution failures, and deploy production systems.
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 Agentic 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 agent architectures, tool protocols, and sprint deliverables
- Technical Director seal and authorized signature
“<<PLACEHOLDER: intern testimonials — Building LangGraph state machines and custom MCP servers during this internship gave me cutting-edge skills that impressed every technical interviewer.>>”
“<<PLACEHOLDER: intern testimonials — The emphasis on error handling, loop termination, and human approval gates showed me what separates toy agents from production software.>>”
4-Step Application & Screening Process
Capped at 15 interns per cohort to ensure deep architectural code reviews and 1-on-1 mentor guidance.
Online Application & Profile Review
Submit your profile, programming background in Python/TypeScript, and GitHub links.
Asynchronous Logic Screening
Complete a lightweight task evaluating tool definition, state manipulation, and async execution.
Agent Architecture Discussion
15-minute briefing with an AI lead to confirm project track, tools (LangGraph/MCP), and milestone roadmap.
Offer Letter & Sprint Kickoff
Receive your official Offer Letter, environment access, API tokens, and attend cohort 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 internship?
Yes. All selected interns receive a formal Offer and Selection Letter detailing track duration, engineering role, mentor assignment, and scope of agentic deliverables.
Is this internship valid for college project submission?
Yes. All documentation, including offer letters, periodic evaluation logs, and final completion certificates, complies with academic project and industrial training requirements.
Can I do it in my final semester?
Yes. Final-year students frequently enrol in our 3-month or 6-month tracks to complete their academic capstone while compiling an advanced portfolio in autonomous AI systems.
Is it work from home or office in Nagpur?
Both formats are available. Candidates can work from our Nagpur center or participate remotely/hybrid with daily async standups and weekly screen-share architecture reviews.
Do I need prior experience with LangGraph or MCP?
No. A solid foundation in Python or TypeScript and REST APIs is all you need. Our mentors provide the architectural blueprints and code review cycles for agent development.
How do I know if I am eligible?
Students and professionals with comfortable programming proficiency who want to build autonomous multi-agent systems and real software are eligible to apply.
What happens after I apply?
Our technical mentors review your application within 24 hours. Shortlisted candidates receive an asynchronous logic screening challenge followed by a track alignment conversation.
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 is the Model Context Protocol (MCP)?
MCP is an open standard that connects AI models to external tools, databases, and environments securely. Interns learn to build and deploy custom MCP servers for agent workflows.
Will my agent projects be publicly verifiable?
Yes. All projects are maintained in public GitHub repositories with MIT or permissive licenses, and your verified certificate includes permanent links directly to your code.
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.