Wand Synthesis AI Inc - Senior Backend Engineer, Context Management
Requirements
• Backend: Python, FastAPI, BlackSheep, Temporal • Data & Search: Elasticsearch, MongoDB, PostgreSQL, Redis, ClickHouse, Snowflake • AI/ML: OpenAI, LangChain, LangGraph, LiteLLM, Docling • Messaging: RabbitMQ, Kafka • Cloud & Infra: Azure, Docker, Kubernetes • Degree in Computer Science, Engineering, or a related field. • 10+ years of engineering experience with a focus on search, information retrieval, or data engineering. • Strong proficiency in Python; willingness to work in additional languages as the stack evolves. • Hands-on experience with search technologies (Elasticsearch, vector databases such as Pinecone, Weaviate, Qdrant, or similar). • 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. • 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). • Background in building enterprise SaaS integrations or source connectors at scale. • Experience with chunking strategies, re-ranking models, and hybrid retrieval approaches. • 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. • Personal Characteristics: • 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.
Responsibilities
• 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. • Integrate with LLM providers and related frameworks (e.g., LangChain, LlamaIndex) to deliver context-aware agent capabilities. • 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.
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