AI Engineer - RAG & Semantic Search
Core
Designing and maintaining vector databases, embedding pipelines, and semantic search mechanisms to power data intelligence solutions for agricultural crop protection.
Role type
Full-Stack Developer (RAG & Semantic Search focus)
Builds
Scalable data intelligence applications and semantic search infrastructure for crop protection solutions
Domain
Agriculture / AI & Machine Learning
Deliverable
production ML models | product features
Required skills
RAG design and evaluation, vector databases (pgvector, Pinecone, Weaviate, Chroma), embedding pipelines, SQL schema design, NLP-to-SQL translation, Python orchestration, semantic search systems
Preferred skills
NLP concepts (tokenization, semantic similarity, NER), evaluation frameworks for retrieval and generation, client-centric solution delivery
Technologies
pgvector, Pinecone, Weaviate, Chroma, OpenAI, Cohere, Sentence Transformers, Python, SQL, LLMs
Responsibilities
Design and maintain vector database architectures for large-scale retrieval; Build and optimize embedding pipelines including document ingestion and chunking; Develop NL-to-SQL interfaces using LLMs; Evaluate and compare RAG pipeline quality metrics; Implement APIs and backend integrations; Contribute to software architecture and infrastructure design
Seniority
Mid-level (3-5 years experience), hands-on IC