Software Engineer (AI)

Location: Tel Aviv, Israel — hybrid

Company

hirify.global develops an AI context layer that provides precise, intent-aware data from code, logs, tickets, and documentation for AI agents and developer workflows.

What you will do

• Design, build, and scale real-time ETL pipelines and indexing engines that unify data from multiple customer integrations into a knowledge graph.
• Develop graph traversal systems for transforming enterprise artifacts into structured context.
• Optimize hybrid retrieval using BM25, dense embeddings, graph expansion, and reranking.
• Build offline and online evaluation frameworks for retrieval accuracy, latency, and agent task success.
• Own core systems end to end, including schema design, implementation, deployment, observability, and production reliability.
• Collaborate on architecture as MCP, multi-agent coordination, and context caching evolve.

Requirements

• 3–5 years of experience building high-performance backend systems, distributed data pipelines, or data-intensive applications.
• Practical experience with embeddings, vector search, lexical search such as BM25, Lucene, or OpenSearch, and reranking trade-offs.
• Hands-on experience using AI coding agents such as Claude Code or Cursor in daily engineering workflows.
• Ability to take ambiguous technical problems from initial concept to production with high ownership.
• Ability to work in a hybrid role based in Tel Aviv.

Nice to have

• Experience with graph databases such as Neo4j, Memgraph, or FalkorDB.
• Familiarity with the Model Context Protocol or agentic execution frameworks.
• Background in search relevance, learning-to-rank, or synthetic evaluation generation.
• Early-stage startup experience.
• B.Sc. in Computer Science or equivalent hands-on experience at strong engineering companies.

Culture & Benefits

• Fast-paced environment with high ownership and end-to-end responsibility.
• Collaborative technical decision-making and openness to different ideas.
• Opportunity to work on deep systems and data engineering problems at enterprise scale.