Eventum AI is an AI engineering consultancy building production-grade LLM, RAG, agent, and document-intelligence systems.
Responsibilities:
- Lead architecture and hands-on implementation of a production RAG / knowledge platform
- Build document ingestion for Google Workspace, PDFs, scans, tables, and images
- Implement permission-aware retrieval with ACL enforcement at query time
- Build hybrid search, embeddings, reranking, and citation-backed generation
- Deploy and benchmark open-weight models using vLLM or similar infrastructure
- Build evaluation/regression systems for retrieval, answer quality, security, and model upgrades
- Own production deployment, observability, CI/CD, documentation, and handoff
- Work directly with Eventum’s senior team and client technical leadership
Requirements:
- 6+ years of professional software/ML engineering experience
- Strong experience building production LLM/RAG systems
- Excellent Python/backend engineering skills
- Hands-on experience with embeddings, vector search, reranking, and document pipelines
- Experience serving open-weight LLMs with vLLM, SGLang, Triton, TGI, or similar
- Strong cloud/infrastructure skills; Azure, Docker, Terraform/Kubernetes, and CI/CD are especially relevant
- Experience with LLM evaluation and production reliability
- Comfortable owning ML, backend, data, and infrastructure rather than working in a narrow specialty
Optional:
- Google Workspace APIs and permissions
- pgvector/Qdrant
- BM25 + dense retrieval
- OCR/document AI
- multimodal models
- Prometheus/Grafana
- no-egress or security-sensitive environments
