P
Solutions Architect
Vector DatabasesRAG ArchitecturePythonKubernetesSemantic SearchLangChainMachine Learning Ops (MLOps)Cloud InfrastructureDistributed Systems
About the role
As a Solutions Architect at Pinecone, you will serve as the primary technical advisor to our most strategic enterprise customers, helping them architectural and scale high-performance vector databases. You will bridge the gap between complex engineering challenges and business value, guiding organizations through the nuances of Retrieval-Augmented Generation (RAG) and semantic search architectures. Your work will directly influence how world-class AI applications are deployed, optimized, and managed in production environments globally.
Responsibilities
- Lead the technical discovery and architectural design for enterprise customers building GenAI applications on the Pinecone platform.
- Develop and showcase production-ready reference architectures for RAG systems, including metadata filtering and hybrid search strategies.
- Partner with Product and Engineering teams to advocate for customer requirements and influence the vector database roadmap.
- Conduct deep-dive performance tuning sessions to optimize query latency and index recall for billion-scale namespaces.
- Create technical content, including blog posts, sample code repositories, and documentation, to empower the Pinecone developer community.
- Provide expert guidance on data ingestion pipelines, ETL processes, and real-time upsert strategies at scale.
Requirements
- 5+ years of experience in technical consulting, solutions architecture, or backend software engineering within cloud environments.
- Proven expertise in search technologies (Elasticsearch, OpenSearch) or distributed database systems.
- Deep understanding of the LLM ecosystem, including frameworks like LangChain, LlamaIndex, and various embedding models.
- Hands-on proficiency in Python and at least one other language such as Go, Java, or Node.js.
- Strong knowledge of cloud infrastructure (AWS, GCP, or Azure) and containerization via Kubernetes.
- Excellent communication skills with the ability to explain high-dimensional geometry and vector indexing concepts to both CTOs and developers.
- Bachelor’s degree in Computer Science, Mathematics, or a related technical field.
Disclaimer: MMagic.ai connects talented people with AI companies around the world. While we work hard to feature quality opportunities, we don't independently verify employers, candidates, salaries, or hiring outcomes. We encourage you to research each opportunity and company before applying or making an offer.