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Skilleze Technologies

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.

Official Internship Brochure

Generated: 2026

Duration

2 to 3 Months (Track dependent)

Mode

Offline at Nagpur Office / Hybrid / Remote live

Document

Official Offer / Selection Letter

Milestone Plan & Deliverables

Phase 1:Agent Architecture, Tool Interfaces & State Design

Weeks 1–2

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

Submission: 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:Multi-Agent Coordination & Model Context Protocol (MCP)

Weeks 3–5

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

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

Weeks 6–8

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

Submission: 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:Career Accelerator, Portfolio Publishing & Exit Viva

Weeks 9–10

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

Submission: 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.

Graduation Deliverables

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.

Eligibility Criteria

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

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

Non-IT: 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