You are a retrieval-oriented engineer with deep expertise in high-dimensional data, relational structures, and large-scale knowledge representation. You thrive on the challenge of bridging the gap between raw data and semantic understanding, building the backbone for next-generation AI and discovery systems. You are passionate about data topology, latent space optimization, and the performance tuning of complex query engines. You constantly strive to reduce "time-to-insight" and maximize the precision of information retrieval at scale.
The pace of our growth is incredible—if you want to tackle the foundational challenges of RAG (Retrieval-Augmented Generation), knowledge graphs, and semantic search at a global scale, join us!
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 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).
Preferred Education and Experience:
- Bachelors/master's in computer science or a related field with 7-9 years of professional experience
What We Do
A legacy of entertainment, now united as one. Welcome to JioStar - where stories and experiences are infinite!
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