Teams at Quantiphi

Architect and implement enterprise MLOps and LLMOps platforms on Google Cloud. Own model lifecycle pipelines, CI/CD automation, deployment, monitoring, drift detection, lineage, governance, and observability. Build GKE-based ML platforms, conduct architecture reviews, troubleshoot complex systems, collaborate with engineering and client teams, and mentor technical staff. Travel up to 30% is required.

Architect and build production-grade multi-agent AI systems, agentic workflows, RAG solutions, tool libraries, memory systems, and evaluation frameworks. Deploy scalable, low-latency solutions across AWS, GCP, or Azure using MLOps, CI/CD, observability, and infrastructure automation. Integrate and optimize LLMs, implement monitoring and safety controls, establish performance metrics, and lead technical delivery while mentoring team members and communicating complex AI concepts to stakeholders.

Designs and productionizes hybrid knowledge graph solutions for healthcare and clinical data using LPG and RDF technologies. Builds FHIR transformation and ingestion pipelines, implements ontology mappings, entity linking, identifiers, provenance, validation, and graph queries with Cypher or SPARQL. Collaborates with ontology, terminology, FHIR, clinical, and data engineering teams, while contributing to architecture reviews, code reviews, testing, and engineering best practices.