Skilleze Technologies
Applied AI engineering internship building production RAG pipelines, fine-tuning open-weights models, implementing guardrails, and deploying LLM applications.
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
Focus: Connecting to state-of-the-art model APIs, implementing semantic chunking, and populating vector databases.
Submission: GitHub repo with document parsing pipeline, vector embedding scripts, and basic similarity search test suite.
Focus: Building hybrid BM25 + dense vector retrieval, cross-encoder reranking, and conversational memory buffering.
Submission: Working RAG pipeline with streaming responses, multi-query generation, and source citation metadata.
Focus: Automated evaluation using RAGAS, input/output guardrail policies, and containerized FastAPI cloud deployment.
Submission: Live deployed API endpoint, interactive Streamlit/React demo UI, and comprehensive evaluation benchmark report.
Focus: Publishing AI project case studies, compiling ATS resume for Generative AI / LLM Engineer roles, and mock interviews.
Submission: Public portfolio page, open-source GitHub release, and completed engineering exit interview.
End-to-end LLM applications with hybrid vector search, context reranking, streaming endpoints, and interactive frontends.
Documented code repositories demonstrating modular Python architecture, vector DB configurations, and evaluation scripts.
Documented test reports proving retrieval faithfulness, answer relevancy, and latency metrics across diverse test sets.
Specialized resume highlighting production LLM implementations, vector architectures, and quantifiable accuracy gains.
Technical mock interviews covering context window trade-offs, vector search scaling, and LLM orchestration challenges.
Official company experience certificate with permanent QR verification linking to your public GitHub contributions.
Year of Study: 2nd, 3rd, or final year students (B.E., B.Tech, BCA, MCA, M.Tech, M.Sc CS/IT/Data Science)
Branches: Computer Science, IT, AI & Data Science, Electronics, or related computational engineering fields
Non-IT: Graduates and professionals with proven Python proficiency and understanding of basic machine learning concepts
Working Professionals: Software developers, data scientists, and backend engineers seeking production Generative AI implementation credentials
Prior Skill Expectation: Intermediate Python programming and familiarity with REST APIs <<CONFIRM per Prompt 0>>; direct entry requires passing an LLM prompting and Python logic assessment