Early-career engineer interested in building LLM-backed systems with backend depth - orchestration, retrieval, observability, and thinking about where AI should and shouldn't make decisions.
Open to Applied AI / AI Engineer roles
Hiver - AI Intern · Dec 2025 – Jun 2026
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Assisted in developing a 5-phase LLM pipeline to surface AI adoption opportunities across the customer base, using batched GPT-5.4 calls with retry backoff, signal-keyword filtering, and PII-aware email preprocessing.
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Supported development of an SQS-based orchestration pipeline computing per-usergroup AI usage metrics across 30/60/90-day windows, publishing feature-wise JSON snapshots to S3 with Redis-precomputed aggregations.
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Contributed to an end-to-end AI automation testing platform (React + FastAPI + Playwright), enabling automated execution of a 15-step Chat Agent scenario with per-run artifact capture (replay video + failure screenshots).
Ernst & Young - Generative AI Intern · Sep 2024 – Nov 2024
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Built a document Q&A RAG pipeline using LangChain, Stella embeddings selected via MTEB leaderboard, and Chroma, with PyMuPDF text extraction, chunking, and top-k retrieval for grounded source-cited answers.
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Benchmarked local Llama 3.1 against Groq-hosted inference on the same pipeline, evaluating the privacy vs. latency tradeoff for sensitive document workloads.
Kreativstorm - Data Analysis Trainee · Sep 2024 – Oct 2024
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Cleaned and analysed 8+ datasets using IBM SPSS, resolving inconsistencies to improve data reliability and produce decision-ready outputs.
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Collaborated with a cross-functional remote team to streamline reporting workflows and deliver data-driven project insights.
🤖 Dossier - Agentic AI research assistant built on a 5-node LangGraph state machine (planner → searcher → reader → synthesizer → critic) with a bounded self-critique loop, citation-grounded reports, Langfuse observability, and a Next.js + MUI frontend. Live ↗
🔍 Hybrid Search System - Hybrid retrieval over 5,753 chunks using dense + sparse search combined via rank-based fusion, Cohere rerank-3.5 cross-encoder, citation-grounded generation, and end-to-end Langfuse tracing. Article ↗
✅ Vendor Onboarding Validator - 6-stage deterministic + LLM hybrid validation pipeline with hard FAIL vs soft WARN logic. LLM restricted to borderline name-mismatch adjudication only - deterministic checks handle all upstream validation.
🎫 TicketSense - Groq-powered LLM ticket triage processing 300 tickets in ~12 minutes, backed by layered async Python architecture and two-tier Postgres + Redis storage.
- Languages: Python · SQL
- AI / LLM: LangChain · LangGraph · Langfuse · RAG · Pinecone · pgvector · Groq
- Backend: FastAPI · Pydantic · SQLAlchemy · SQS · AWS (S3, Athena)
- Databases: PostgreSQL · Redis · MySQL · Elasticsearch
- Infra: Docker · Git
- Agentic AI - LangGraph state machines, bounded self-critique loops, tool-use orchestration
- Hybrid retrieval - dense + sparse fusion (RRF) and cross-encoder reranking
- Deterministic-first AI design - LLMs for ambiguity, deterministic logic for everything else
- LLM observability - Langfuse tracing across every node, token cost and latency per request
- Citation-grounded generation - making LLM answers traceable to source chunks