Backend engineer for context management

Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations. Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.

• Design and build scalable search and retrieval systems combining lexical and semantic approaches.
• Develop and maintain connectors to enterprise data sources (SaaS platforms, data warehouses, document stores, APIs).
• Build data pipelines that ingest, transform, and index customer data for use by AI agents.
• Pull and process analytics data from customers' warehouses (Snowflake, BigQuery, Databricks, etc.).
• Own projects end-to-end: from architecture and technical design through implementation, deployment, and ongoing maintenance.
• Collaborate with product and AI teams to translate retrieval quality into measurable agent performance improvements.
• Optimize retrieval pipelines for latency, relevance, and cost efficiency at scale.
• Uphold a culture of high efficiency, creativity, and quality.

• Degree in Computer Science, Engineering, or a related field.
• 10+ years of engineering experience
• Strong proficiency in Python or Golang; willingness to work in additional languages as the stack evolves.
• Background in building enterprise SaaS integrations or source connectors at scale, or hands-on experience with search technologies (Elasticsearch, vector databases such as Pinecone, Weaviate, Qdrant, or similar).
• Proficiency in at least one cloud environment (GCP, AWS, Azure).
• Proven track record in a high-paced startup environment.
• Self-sufficiency across the stack, comfortable operating without dedicated DevOps support.
• Experience with containerized environments (Docker, Kubernetes).
• Strong individual contributor comfortable owning major projects with minimal oversight.
• Thinks architecturally: balances long-term design quality with startup speed.
• Excellent communication and interpersonal skills.
• Continuous drive for improvement and innovation.
• Будет плюсом: Solid understanding of embeddings, semantic search, and retrieval-augmented generation (RAG) patterns; Experience building and maintaining data pipelines and ETL/ELT workflows; Familiarity with at least one major data warehouse platform (Snowflake, BigQuery, Databricks, Redshift); Experience working with LLM APIs and agent frameworks in production; Experience with chunking strategies, re-ranking models, and hybrid retrieval approaches; Experience on search, information retrieval, or data engineering; Familiarity with data governance, access control, and multi-tenant data architectures; Contributions to open-source search or retrieval projects; Experience with production systems serving enterprise customers

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