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Light IT Global

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Design & build LLM applications: RAG over client KBs, multi-tool/agent graphs, prompt strategies, and voice UX (STT/TTS)
Implement retrieval & data flows: ingestion, chunking, metadata/embeddings, hybrid retrieval, re-ranking, caching
Own evaluation & quality: latency/cost budgets, A/Bs
Automate with n8n (or similar): connect services, data, and back-office workflows
expose reliable admin ops for non-engineers
Ship pipelines: Airflow for orchestration
dbt for transformations/semantic layer where relevant
Productionize: tracing/observability, feature flags, safe rollouts, secrets & config hygiene
Collaborate with clients: discover scope, explain trade-offs, demo progress
optional presales spikes/estimates

Strong Python (typing, tests, packaging) and web/service basics (FastAPI, Docker)
Hands-on building LLM apps with one or more: LangChain, LangGraph, AutoGen, Dify (or equivalents)
RAG end-to-end experience: corpus prep → embeddings → retrieval → re-rank → context assembly → measurable gains
Evaluation mindset: design offline eval sets and release gates
reason about faithfulness vs relevancy
control p95 latency & $/session
Cloud proficiency in AWS or Azure (using at least one in production)
Comfort with observability (OpenTelemetry/LangSmith/Prometheus/Grafana or similar)
English B2+
clear written & verbal client communication

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