Alpha Business Process Automation(RAG Engineer), Assistant Manager
State Street CorporationMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessNiche RAG and knowledge-graph skills reduce candidate pool, but metro location and known employer increase competition.
Requires deep knowledge-graph, RAG, and agentic AI expertise, limiting cross-industry transferability.
Explicit 7–12 years plus mandatory RAG, graph, Python, and cloud skills enforce strict technical filtering.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, integrate, test, deploy, and support enterprise-scale Ontologies, Knowledge Graphs, RAG, and Graph RAG capabilities with a focus on knowledge and retrieval engineering (~80% of role).
Develop and maintain ingestion and transformation pipelines for structured and unstructured data, implement hybrid retrieval, semantic search, grounding, source attribution, and evaluation of RAG solutions.
Collaborate cross-functionally with architects, AI platform, product, and engineering teams to deliver secure, reliable, measurable AI knowledge and retrieval services including some involvement (~20%) in AI orchestration, agentic AI, cloud-native services, and governance.
Minimum Requirements
Bachelor's degree in Computer Science, Engineering, AI, Data Science, Information Systems, or related discipline.
7 to 12 years of overall technology experience with 3+ years hands-on experience developing or supporting enterprise data, semantic, graph, search, knowledge, or AI solutions.
Hands-on experience with at least one enterprise graph platform (e.g., Neo4j, Amazon Neptune) and proficiency in graph query languages such as Cypher or SPARQL.
Strong Python programming skills for developing REST APIs or microservices with production-quality code, plus knowledge of RDF, RDFS, OWL, SHACL or labeled property graphs.
Ideal Candidate Profile
Experienced in building end-to-end enterprise knowledge and retrieval systems including ontology design, knowledge graph engineering, and complex RAG pipelines in regulated or enterprise environments.
Skills bridging deep semantic technology knowledge with practical cloud-native AI orchestration and operational support in an Agile, collaborative setting.
Ability to communicate complex AI, graph, and data concepts effectively with varied stakeholders and deliver governed, traceable, and high-quality production AI knowledge services.