Company
Data Platform
StaffSoftwareDevelopmentEngineer(DataEngineer)
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff Software Development Engineer (Data Engineer). Skills: Data Engineering, high-performance data retrieval and storage, Vector Databases, Graph Databases, LLM orchestration frameworks, embedding models, large-scale data processing. Lead the design and development of hybrid retrieval architectures combining vector similarity search with structured graph traversals. Architect scalable data pipelines for the ingestion, embedding, and indexing of massive, multi-modal datasets”
What You'll Achieve.
ensuring high-performance relationship mapping and ontological integrity; monitor the quality of embeddings and the health of the vector space; focusing on long-term persistence, observability, and sub-second latency; ensuring that disparate data sources are unified into a coherent, searchable knowledge base
Industry & Context.
What They're Looking For.
Must Have
7+ years of experience in data engineering or backend systems with a focus on high-performance data retrieval and storage, BE. Tech in Computer Science, Mathematics, or equivalent, Expert proficiency in Python, Java, or Go, with a grasp of distributed system design patterns, Deep understanding of Vector Databases, including indexing strategies (HNSW, IVFFlat, PQ) and distance metrics (Cosine, Euclidean, Dot Product), Experience with Pinecone, Milvus, Weaviate, or Qdrant, background in Graph Databases (Neo4j, AWS Neptune, or ArangoDB) and query languages like Cypher or Gremlin, Experience with Data Modeling and organization, specifically in building semantic layers, ontologies, and taxonomies, Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex) and embedding models (OpenAI, HuggingFace, Cohere), Proficiency in large-scale data processing using Spark, Flink, or Kafka for real-time indexing and ETL, Understanding of Information Retrieval (IR) fundamentals, including BM25, TF-IDF, and reciprocal rank fusion, Experience with cloud-native infrastructure (AWS/GCP/Azure) and container orchestration (Kubernetes)
Nice to Have
MS or PhD in a related field is a plus, Bachelors/master's in computer science or a related field with 7-9 years of professional experience
What You'll Do.
Lead the design and development of hybrid retrieval architectures combining vector similarity search with structured graph traversals
Architect scalable data pipelines for the ingestion
and indexing of massive
Innovate and prototype advanced retrieval techniques
including multi-stage re-ranking
graph-tooling for LLMs
and dynamic metadata filtering
Design and implement schemas for complex knowledge graphs
ensuring high-performance relationship mapping and ontological integrity
Build automated data validation and drift detection systems to monitor the quality of embeddings and the health of the vector space
Drive technical implementation of "Memory" systems for AI agents
focusing on long-term persistence
and sub-second latency
Champion data organization standards
ensuring that disparate data sources are unified into a coherent
searchable knowledge base
Collaborate with AI Research and Product teams to evaluate emerging database technologies (e. g.
GraphRAG) and integrate them into production
How You'll Work.
Team & Collaboration
Collaborate with AI Research and Product teams
Full Job Description
## Key Responsibilities Lead the design and development of hybrid retrieval architectures combining vector similarity search with structured graph traversals. Architect scalable data pipelines for the ingestion, embedding, and indexing of massive, multi-modal datasets. Innovate and prototype advanced retrieval techniques, including multi-stage re-ranking, graph-tooling for LLMs, and dynamic metadata filtering. Design and implement schemas for complex knowledge graphs, ensuring high-performance relationship mapping and ontological integrity. Build automated data validation and drift detection systems to monitor the quality of embeddings and the health of the vector space. Drive technical implementation of "Memory" systems for AI agents, focusing on long-term persistence, observability, and sub-second latency. Champion data organization standards, ensuring that disparate data sources are unified into a coherent, searchable knowledge base. Collaborate with AI Research and Product teams to evaluate emerging database technologies (e.g., HNSW optimizations, GraphRAG) and integrate them into production. ## Skills and Attributes for Success 7+ years of experience in data engineering or backend systems with a focus on high-performance data retrieval and storage. BE/B.Tech in Computer Science, Mathematics, or equivalent. MS or PhD in a related field is a plus. Expert proficiency in Python, Java, or Go, with a strong grasp of distributed system design patterns. Deep understanding of Vector Databases, including indexing strategies (HNSW, IVFFlat, PQ) and distance metrics (Cosine, Euclidean, Dot Product). Experience with Pinecone, Milvus, Weaviate, or Qdrant. Strong background in Graph Databases (Neo4j, AWS Neptune, or ArangoDB) and query languages like Cypher or Gremlin. Experience with Data Modeling and organization, specifically in building semantic layers, ontologies, and taxonomies. Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex) and embedding
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