Industrial Work Experience
Domain Experience Track

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.

Duration2 to 3 Months
ModeOffline (Nagpur) / Hybrid / Remote
CredentialVerifiable Experience Certificate
Code ReviewsSenior AI Systems Lead Reviews
Key Facts SummaryExtractable Entity Specifications
Institute EntitySkilleze Technologies
Location & CampusNagpur, Maharashtra (Ramdaspeth / Central Nagpur)
Training & Delivery ModeOffline at Nagpur Office / Hybrid / Remote live
Program Duration2 to 3 Months (Track dependent) (P2M)
Credential AwardedOfficial Offer / Selection Letter
Quick Answer · What This Internship Is & Who It Is For

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.

Admission & Criteria

Eligibility & Who Can Apply

Explicit candidate eligibility criteria for students, graduates, and working professionals.

Eligible Year of Study

3rd or final year students (B.E., B.Tech, MCA, M.Tech, M.Sc CS/IT)

Eligible Academic Branches

Computer Science, IT, Artificial Intelligence, Electronics, or related technical engineering streams

Non-IT & Career Switchers

Technical graduates or professionals with demonstrated software engineering experience in Python or TypeScript

Working Professionals

Software engineers and system architects seeking production expertise in autonomous agents and MCP tooling

Prior Skill Expectation:Comfortable proficiency in Python or TypeScript, asynchronous programming, and REST/JSON concepts <<CONFIRM per Prompt 0>>; direct entry requires passing an agentic workflow screening challenge
Program Parameters

Internship Specifications At-a-Glance

Key operational parameters, batch structures, and documentation provided.

Program Duration2 to 3 Months (Track dependent)
Weekly Commitment20–25 hours per week (Flexible evening/weekend slots available)
Work ModeOffline at Nagpur Office / Hybrid / Remote live
Reporting ExpectationsDaily async Slack standups, weekly multi-agent design walkthrough and code review calls
Cohort Start Dates1st and 15th of every month (Cohort capacity capped at 15 interns)
Documents Provided
  • Official Offer / Selection Letter
  • Internship Completion Certificate
  • Internship Experience Certificate (with verifiable GitHub project URLs)
  • College-format industrial training / project report letter <<CONFIRM: university formats supported>>
  • NOC compliance documentation for college submission
Real Engineering Operations

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

Production Project 1

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.

PythonLangGraphModel Context Protocol (MCP)DockerGitHub APIFastAPI
Shipped Deliverable:Autonomous agent workflow with stateful checkpoints, rollback triggers, human approval gates, and automated PR generation.
Production Project 2

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.

TypeScriptLangGraph.jsMCP ToolsPostgreSQLNext.js
Shipped Deliverable:Production agentic workflow complete with real-time streaming execution graph visualization and audit trails.
Ticket / Sprint Workflow

Sprints focus on agent architecture challenges: state graph design, memory persistence, tool definition contracts, loop termination policies, and error recovery strategies.

Git Branch & PR Process

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.

Code Review Cadence

Senior AI architects review code within 48 hours, inspecting loop termination guarantees, context state boundaries, tool call reliability, and token spend caps.

Standup & Demo Rhythm

Daily 3-question async standup (agent behaviors tested, tool bugs resolved, blockers) plus weekly live swarm architecture demonstrations.

Engineering Definition of Done (DoD)

Agent state machine handles infinite loops, tool timeouts, and error states gracefully
Human-in-the-loop approval gates integrated for critical external actions
Custom MCP server passes standardized protocol compliance checks
Deployed to cloud infrastructure with session persistence and streaming UI
Architecture documentation, state diagram, and evaluation metrics in repo README
Milestone Gates

Phase-by-Phase Deliverable Milestone Plan

Gate-reviewed deliverables submitted at each milestone. This is a deliverable roadmap, not a classroom topic list.

Phase 1 · Weeks 1–2

Agent Architecture, Tool Interfaces & State Design

Understanding agent loops (ReAct pattern), building custom tool interfaces, and setting up LangGraph state schemas.

Submitted:GitHub repository with basic tool execution framework, state persistence setup, and unit test suite.
Gate Review Criteria

Lead review of state isolation, tool validation schemas, and error catching.

Phase 2 · Weeks 3–5

Multi-Agent Coordination & Model Context Protocol (MCP)

Building supervisor/worker topologies, inter-agent message routing, and implementing custom MCP servers.

Submitted:Multi-agent orchestration pipeline communicating with custom MCP tools across isolated execution contexts.
Gate Review Criteria

Architecture review evaluating delegation logic, context pollution prevention, and protocol compliance.

Phase 3 · Weeks 6–8

Memory Persistence, Human-in-the-Loop & Evaluation

Implementing long-term episodic memory, human approval pause/resume gates, and agent trajectory evaluations.

Submitted:Live deployed agent system with interactive control dashboard, evaluation benchmark runs, and Docker container.
Gate Review Criteria

Production readiness audit inspecting self-healing behavior and cost control.

Phase 4 · Weeks 9–10

Career Accelerator, Portfolio Publishing & Exit Viva

Assembling GitHub agentic portfolio, tuning ATS resume for AI Systems / Agent Engineer roles, and mock technical viva.

Submitted:Public portfolio page, open-source GitHub release, and completed engineering exit interview.
Gate Review Criteria

Final evaluation by technical director and issuance of verified experience certificate.

Evaluation Standard

Mentorship, Code Reviews & Performance Evaluation

Reviewer Profile

Principal AI Engineers and Systems Architects with direct experience building production agent workflows and distributed systems.

Review Cadence

Continuous GitHub PR reviews (within 48 hours) plus weekly 1-on-1 agent architecture reviews and live tracing sessions.

How Performance Is Graded
  • 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
What Earns a Strong Recommendation

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.

Tangible Proof

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.

Academic Compliance

For College Students: Industrial Training & University Credit

Academic Credit Compliance

Complies with university industrial training, summer internship, and final-semester major project requirements.

Submission Formats Provided

We supply company offer letters, synopsis documents, guide evaluation reports, and formal project completion books.

Semester-Break & Exam Scheduling

Flexible sprint scheduling built around semester exams, viva examinations, and university schedules.

Nagpur University Guidelines

Structured to satisfy industrial training mandates across RTMNU, YCCE, VNIT, RCOEM, and GHRCE <<CONFIRM: which university formats you support — no false claims of affiliation>>.

Career Transition

For Working Professionals: Evening/Weekend Project Sprints

Flexible Sprint Scheduling

Async task assignment and flexible weekend review sessions allow employed engineers to build agentic AI credentials without disrupting their current job.

Portfolio-for-Switching Angle

Positions you ahead of the industry curve with verifiable multi-agent implementations and MCP expertise that few developers possess.

How It Differs From a Course

Instead of watching introductory agent demos, you engineer resilient state graphs, debug tool execution failures, and deploy production systems.

Track Selection

Track Comparison: 45 Days vs 2 vs 3 vs 6 Months

Compare duration tracks by project deliverables, mentorship hours, and Career Accelerator inclusion.

Track Feature45-Day Track2-Month Track3-Month Accelerator6-Month Incubation
Shipped Projects1 Production Project2 Production Projects3 Production Projects3 Enterprise Systems
Engineering DepthFast-Track DeliveryModular Feature + CapstoneMulti-Tier Cloud ServicesDistributed Architecture & Microservices
PR Mentorship Hours15+ Dedicated Hours30+ Dedicated Hours45+ Dedicated Hours80+ Dedicated Hours
Career AcceleratorResume Entry OnlyResume + Mock VivaFull Module IncludedFull Module + System Design
Best-Fit PersonaVacation training students2nd/3rd year engineersFinal years & career switchersFull-semester college capstone
Explore Track45-Day Details2-Month Details3-Month Details6-Month Details
Proof & Verification

Verifiable Proof: Projects, Certificate & Intern Feedback

All credentials link to tamper-proof QR codes and public GitHub contributions.

Verified Experience Credential Sample

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.>>”

Vikas Meshram · Agentic AI Engineer Intern (RCOEM Nagpur)

“<<PLACEHOLDER: intern testimonials — The emphasis on error handling, loop termination, and human approval gates showed me what separates toy agents from production software.>>”

Tanvi Agarwal · AI Systems Intern (VNIT Nagpur)
Application Timeline

4-Step Application & Screening Process

Capped at 15 interns per cohort to ensure deep architectural code reviews and 1-on-1 mentor guidance.

1Day 1

Online Application & Profile Review

Submit your profile, programming background in Python/TypeScript, and GitHub links.

2Days 2–3

Asynchronous Logic Screening

Complete a lightweight task evaluating tool definition, state manipulation, and async execution.

3Day 4

Agent Architecture Discussion

15-minute briefing with an AI lead to confirm project track, tools (LangGraph/MCP), and milestone roadmap.

4Day 5

Offer Letter & Sprint Kickoff

Receive your official Offer Letter, environment access, API tokens, and attend cohort kickoff.

Selection Criteria:Solid command of Python or TypeScript with async programming conceptsCommitment to minimum 20 hours per week of dedicated engineering workEagerness to pioneer emerging autonomous AI architectures
Got Questions?

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.

Need the fundamentals first? Start with the Agentic AI Course

Ready to Build Production Software?

Join our next cohort, receive senior PR code reviews, and graduate with verifiable GitHub proof for recruiters.

Internal Linking Matrix

Discover related engineering skill courses, industrial internship programs, and career frameworks at Skilleze Technologies.

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