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Aishwarya Thorat
Intern Data Scientist specializing in ML engineering and LLM agentic workflows
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About
Built an agentic, multi-step LLM system that generates full-stack code for API integrations using LangChain orchestration, Pinecone/SentenceBERT RAG, and a human-in-the-loop feedback loop for iterative code refinement. Also collaborated with non-technical content writers and PMs during a Contentstack internship to deliver a Slack-based AI workflow that generates and brand-checks articles with one-click approvals.
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Key Strengths
- Designed and built an end-to-end multi-agent code-generation system (parser + RAG + planner + codegen) with user feedback loop
- Strong structured-output reliability practices (Pydantic schemas, strict prompting, JSON repair, targeted retries)
- RAG implementation experience using SentenceBERT + Pinecone with API corpus stored in Couchbase Capella
- Workflow decomposition and independent agent testing for reliability (edge cases, missing params, long inputs)
- Practical orchestration and deployment mindset (LangChain orchestration; Docker/Kubernetes; planning FastAPI backend + auth/persistence)
- Effective collaboration with non-technical stakeholders; delivered Slack-based AI content workflow with approval loop
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