Domain Intelligence Engineer — Document Management Platform (Vice President)

Help shape how enterprise document management technology is evaluated, adopted, and governed at scale. In this role, you will build practical artificial intelligence tooling that makes governance faster, more consistent, and evidence-based. You will partner closely with platform engineers and architecture stakeholders to improve decision quality and reuse across the organization. Your work will directly influence platform standards, capability definitions, and technology assessments.

As a Vice President Domain Intelligence Engineer in Document Management Platform, you own day-to-day domain governance and build intelligent tooling that scales how document management technologies are assessed and guided. You evaluate inbound technology requests, maintain the domain capability taxonomy, and identify gaps between demand and platform capabilities. You design and maintain a governed knowledge base and build Retrieval-Augmented Generation (RAG) and agentic workflows that accelerate assessments with documented, traceable outputs. You communicate recommendations clearly and support architecture forums with consistent, repeatable evaluation methods.

Within the first 90 days, you will align key platform initiatives to documented capabilities, complete initial technology assessments using the standard rubric, and publish an initial knowledge base for platform engineering users. Within six months, you will deliver an automated triage workflow that matches requests to capabilities and produces draft, rubric-based recommendations to reduce turnaround time. Within 12 months, you will establish the knowledge base as the trusted reference for domain decisions and keep the capability taxonomy current and defensible for architecture and information security stakeholders.

Job responsibilities

- Serve as the document management domain intelligence lead, owning technology preferences and decision guidance for the domain.
- Evaluate inbound technology requests (vendor, open-source, and internally developed), map requests to existing capabilities, and route items for further review when needed.
- Maintain and improve the domain capability taxonomy, ensuring capabilities are current, clearly described, and consistently classified.
- Identify capability gaps between platform offerings and engineering demand, and translate gaps into structured inputs for the platform roadmap.
- Apply a rubric-based assessment method consistently, producing documented, defensible recommendations and decision records.
- Design and maintain a governed, queryable knowledge base covering capability definitions, assessment history, decision records, and platform non-functional requirements.
- Implement RAG pipelines over authoritative documentation to deliver accurate, citation-backed responses for engineers and stakeholders.
- Develop agentic evaluation workflows that retrieve relevant capabilities and prior assessments, draft preliminary recommendations, and score requests against the rubric.
- Build and maintain natural-language query experiences that help users understand capabilities, preferences, and outcomes with governed responses.
- Instrument governance and assessment workflows with measurable metrics (for example, throughput, coverage, and taxonomy drift) and publish reporting for governance stakeholders.
- Create proofs of concept when needed to validate assessments and demonstrate whether requests align to existing services or require new capability investment.

Required qualifications, capabilities, and skills

- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field.
- Seven or more years of relevant experience in technology governance, architecture review, or platform engineering within a large enterprise environment.
- Proficiency in Python and experience building production-quality automation, evaluation pipelines, or workflow services.
- Experience designing and building knowledge bases and RAG solutions, including chunking strategies, embeddings, semantic retrieval, and grounded response generation.
- Working knowledge of vector search and storage technologies (for example, OpenSearch or Pinecone) and large language model platforms (for example, Amazon Bedrock or Azure OpenAI).
- Understanding of the applied artificial intelligence lifecycle, including prompt design, evaluation, and output governance for document processing use cases.
- Knowledge of enterprise content and document management concepts, such as capture, metadata management, lifecycle governance, retention, and search.
- Experience with cloud services used for document intelligence workflows (for example, object storage, event streaming, and document extraction services).
- Ability to apply structured, rubric-based evaluation methods and explain recommendations in governance forums.
- Ability to write clear capability descriptions, assessment reports, and decision documentation for architecture audiences.
- Demonstrated ability to manage competing priorities and deliver high-quality outputs under time constraints.

Preferred qualifications, capabilities, and skills

- Experience in regulated industries supporting content in legal, compliance, risk, or operations contexts.
- Experience using an enterprise technology governance intake and review platform.
- Familiarity with domain model structures (category, domain, capability) and technology lifecycle stages (invest, maintain, divest, decommission).
- Experience building agentic workflows or multi-agent systems using common orchestration frameworks (for example, LangChain or LlamaIndex).
- Familiarity with graph databases (for example, Amazon Neptune or Neo4j) for knowledge graph development and relationship querying.
- Experience with intelligent document processing capabilities such as optical character recognition, classification, and extraction validation.
- Relevant cloud or generative artificial intelligence certification.