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The Principal Knowledge Graph Engineer designs, builds, and operationalizes enterprise-grade knowledge graph capabilities that connect data, concepts, and relationships to power AI-driven experiences such as search, recommendations, analytics, and agentic workflows. This role blends deep graph engineering, semantic modeling, and production software engineering to deliver a governed, performant, and evolvable “knowledge layer” across products and internal platforms.

The **Lead MLOps Engineer** designs, builds, and runs the production-grade systems that reliably deliver machine learning models into customer-facing and internal products. This role turns research-quality models into **secure, observable, scalable, cost-efficient** services and pipelines, while establishing repeatable standards for model delivery and operations across the AI & ML department.

A **Knowledge Graph Engineer** designs, builds, and operates knowledge graph (KG) systems that connect disparate enterprise data into a unified, queryable, and semantically meaningful representation. The role blends data engineering, graph modeling, ontology design, and applied AI techniques to enable better search, recommendations, analytics, reasoning, and AI applications (including LLM-augmented experiences).

The **Associate RAG Engineer** builds and improves retrieval‑augmented generation (RAG) capabilities that connect large language models (LLMs) to trusted enterprise knowledge (documents, tickets, product data, policies) to produce accurate, grounded answers. This role focuses on implementing retrieval pipelines, preparing and indexing content, evaluating answer quality, and supporting productionization under guidance of senior engineers.

The Associate Knowledge Graph Engineer designs, builds, and maintains foundational knowledge graph assets—schemas, pipelines, entity resolution logic, and query interfaces—that connect enterprise data into a semantically consistent graph for AI and ML use cases. This role focuses on delivering reliable graph-ready datasets, improving graph data quality, and enabling downstream applications such as semantic search, recommendations, analytics, and emerging LLM-powered experiences.

The AI Governance Engineer designs, implements, and operates the technical controls that ensure AI/ML systems are safe, compliant, auditable, and aligned with organizational policy throughout their lifecycle—from data intake and model training to deployment, monitoring, and decommissioning. This role sits at the intersection of engineering, risk, and responsible AI, translating governance requirements into automated guardrails, tooling, and repeatable processes that integrate directly into ML and software delivery pipelines.