AI Engineer (Applied / Production)

We are a Singapore-based technology company building for parts of the economy the industry has long overlooked. We bring serious engineering including modern AI and machine learning to problems most companies have treated as too hard, too small, or too messy to be worth it. Zillwork is one of these. It focuses on a vast and underserved part of the global economy a workforce that is in demand yet poorly served by the systems built around it. Zillwork is building the infrastructure to change that. The problems we've taken on are genuinely hard, the impact is real, and the people we're building for have been overlooked for too long. If that's the kind of work you've been looking for, we'd like to meet you. Own applied intelligence in the live product: retrieval, matching, and serving production engineering, not research. You'll own A RAG pipeline: chunking, embedding generation, vector search (HNSW/IVF-class, pgvector/managed vector DB), reranking, and LLM reasoning behind clean interfaces.Matching/ranking: candidate generation + learning-to-rank across multi-signal features (geo, skill, availability).Model serving: REST/gRPC inference endpoints, batching, caching, autoscaling, latency/cost SLOs, observability.Integrating an on-device inference path alongside server inference.NLP/voice features for an Indic language: intent, entity extraction, semantic search.Must-have 4+ yrs building AI-powered products served in production.Strong Python; production LLM/RAG; embeddings + vector databases (HNSW/IVF indexing).Inference serving (FastAPI/Triton/TorchServe-class); latency/throughput optimization.NLP fundamentals; API design; caching and queueing.Strong signals Learning-to-rank / recommendation systems; multilingual/low-resource NLP; semantic search; on-device inference integration; prompt/reasoning orchestration.Auto-disqualify: notebook-only, nothing served pure-research profile can't explain a vector index AI-generated application. We are a Singapore-based technology company building for parts of the economy the industry has long overlooked. We bring serious engineering including modern AI and machine learning to problems most companies have treated as too hard, too small, or too messy to be worth it. Zillwork is one of these. It focuses on a vast and underserved part of the global economy a workforce that is in demand yet poorly served by the systems built around it. Zillwork is building the infrastructure to change that. The problems we've taken on are genuinely hard, the impact is real, and the people we're building for have been overlooked for too long. If that's the kind of work you've been looking for, we'd like to meet you. Own applied intelligence in the live product: retrieval, matching, and serving production engineering, not research. You'll own A RAG pipeline: chunking, embedding generation, vector search (HNSW/IVF-class, pgvector/managed vector DB), reranking, and LLM reasoning behind clean interfaces.Matching/ranking: candidate generation + learning-to-rank across multi-signal features (geo, skill, availability).Model serving: REST/gRPC inference endpoints, batching, caching, autoscaling, latency/cost SLOs, observability.Integrating an on-device inference path alongside server inference.NLP/voice features for an Indic language: intent, entity extraction, semantic search.Must-have 4+ yrs building AI-powered products served in production.Strong Python; production LLM/RAG; embeddings + vector databases (HNSW/IVF indexing).Inference serving (FastAPI/Triton/TorchServe-class); latency/throughput optimization.NLP fundamentals; API design; caching and queueing.Strong signals Learning-to-rank / recommendation systems; multilingual/low-resource NLP; semantic search; on-device inference integration; prompt/reasoning orchestration.Auto-disqualify: notebook-only, nothing served pure-research profile can't explain a vector index AI-generated application.