and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and More ❯
stack and AI development. Experience: 5+ years of full-stack development experience. Strong proficiency in Python (FastAPI) and React/Next.js. Experience with RAG systems, vector databases (pgvector, FAISS, Weaviate), and hybrid search. Deep understanding of chunking, indexing, reranking, and evaluation metrics. Solid experience with SQL and NoSQL databases (Postgres, DynamoDB). Familiarity with AI/ML models and APIs More ❯
on engineer with an ownership mindset, strong communication skills, and a collaborative approach. 5+ years experience in full-stack development. Strong background in RAG systems , vector databases (pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods. Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques. Proficient in Python (FastAPI) and React/ More ❯
on engineer with an ownership mindset, strong communication skills, and a collaborative approach. 5+ years experience in full-stack development. Strong background in RAG systems , vector databases (pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods. Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques. Proficient in Python (FastAPI) and React/ More ❯
on engineer with an ownership mindset, strong communication skills, and a collaborative approach. 5+ years experience in full-stack development. Strong background in RAG systems , vector databases (pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods. Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques. Proficient in Python (FastAPI) and React/ More ❯
units. What were looking for: Strong background in software engineering (Python, Java, or similar). Hands-on experience with AI/ML frameworks and tools (LangChain, Hugging Face, LlamaIndex, Weaviate, etc.). Experience deploying AI systems in production environments. Excellent systems thinking, problem-solving, and collaboration skills. This is a chance to shape the future of AI in one of More ❯
