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Build AWS-based knowledge engineering solutions spanning knowledge graphs, ontologies, semantic models, data pipelines, vector search, and LLM grounding. Develop and deploy components using Neptune, SageMaker, Bedrock, and related AWS services. Apply graph, NLP, search, generative AI, and RAG techniques to enterprise problems, while collaborating with clients and delivery teams. Document designs, validate solutions, support demonstrations, and mentor junior engineers.

Designs and leads enterprise knowledge engineering solutions using Databricks, knowledge graphs, ontologies, semantic layers, vector search, and LLM grounding. Builds scalable lakehouse pipelines, AI applications, governed knowledge services, and graph-based solutions. Leads workstreams and teams, mentors engineers, defines technical standards, collaborates with clients and cross-functional stakeholders, and supports consulting, pre-sales, solution shaping, and thought leadership.

Designs knowledge frameworks for AI systems, including ontologies, knowledge graphs, and semantic models. Translates expert and unstructured knowledge into structured representations, applies analytics and reasoning techniques to graph data, and supports automation and actionable insights. Leads knowledge representation development, collaborates across teams, makes technical decisions, mentors junior staff, and serves as a subject matter expert. Requires Neo4j expertise, graph database design skills, and 7.5 years of experience.

Design and lead enterprise knowledge engineering solutions using AWS, knowledge graphs, ontologies, semantic layers, vector search, and LLM grounding. Build scalable graph ingestion and AI data pipelines, APIs, RAG systems, and governed knowledge services. Guide technical direction, mentor teams, collaborate with clients and cross-functional stakeholders, and contribute to consulting, pre-sales, solution shaping, and thought leadership.

Leads the design and implementation of AWS-based knowledge engineering solutions, including knowledge graphs, ontologies, semantic layers, graph ingestion pipelines, vector retrieval, and LLM grounding. Translates business problems into scalable AI solutions, guides technical direction, mentors teams, and manages major workstreams. Collaborates with clients, architects, engineers, product teams, and domain experts while contributing to solution shaping, proposals, thought leadership, and enterprise delivery standards.

Leads the design and implementation of enterprise knowledge engineering solutions on GCP, including knowledge graphs, ontologies, semantic layers, AI pipelines, vector search, RAG, and LLM grounding. Builds scalable graph ingestion and data pipelines using GCP services, guides architecture and technical direction, mentors engineers, collaborates with clients and cross-functional teams, and contributes to consulting, proposals, pre-sales, reusable assets, and thought leadership.

Lead enterprise knowledge engineering and AI architecture initiatives on AWS. Design knowledge graphs, ontologies, semantic layers, governed data products, graph ingestion pipelines, vector integrations, and LLM grounding solutions. Establish architecture standards, lead governance and solution design, support pre-sales and client engagements, manage technical risks, and mentor multidisciplinary engineering teams. Apply AWS services, graph technologies, machine learning, RAG, and agentic AI patterns to deliver reusable, scalable solutions across industries.

Leads enterprise knowledge engineering initiatives involving knowledge graphs, ontologies, semantic layers, AI applications, LLM grounding, RAG, and cloud-native data pipelines on GCP. Designs scalable graph and vector solutions, guides technical direction, manages workstreams, mentors engineers, collaborates with clients and cross-functional teams, and supports consulting, proposals, pre-sales, and thought leadership.

Leads enterprise knowledge engineering and AI architecture programs using knowledge graphs, ontologies, semantic layers, and AWS cloud services. Designs graph ingestion pipelines, LLM grounding, RAG systems, vector integrations, governed data products, and scalable knowledge services. Owns architecture standards, governance, solution estimation, stakeholder engagement, pre-sales, delivery quality, and technical risk management. Builds reusable assets, mentors multidisciplinary teams, and translates complex industry needs into production-ready AI and knowledge graph solutions.

Leads enterprise knowledge engineering architecture on GCP, designing knowledge graphs, semantic layers, ontologies, data pipelines, vector integrations, and LLM grounding solutions. Defines architecture standards, governs delivery, manages technical risks, supports pre-sales, mentors multidisciplinary teams, and translates complex business needs into scalable AI knowledge applications. Requires deep expertise in knowledge graph technologies, cloud data services, AI/ML pipelines, semantic modeling, governance, and stakeholder leadership.