We operate as one global team across the North America, EMEA, and APAC, combining process engineering, automation delivery, AI platform engineering and responsible-AI governance under a single roof. We work on real client implementations, not pilots that sit on a shelf — what we build is adopted, measured and scaled. If you want to shape how AI is applied to one of the most complex delivery landscapes in financial services, this is the team to do it in.
What you will be responsible for
Own the enterprise architecture for Knowledge Engineering, Data Engineering, RAG and Graph RAG, Agentic AI, testing, and governance, aligning strategy, platforms, delivery, controls, and measurable outcomes.
Define the enterprise vision, target architecture, standards, reusable patterns, and roadmap across data, knowledge, retrieval, AI agents, testing, and governance.
Translate business priorities into scalable solution architectures, transition plans, investment decisions, and measurable outcomes.
Lead knowledge and data architecture, including ontologies, semantic models, knowledge graphs, metadata, lineage, data pipelines, quality, and lifecycle management.
Architect production-grade RAG and Graph RAG services covering ingestion, indexing, hybrid retrieval, ranking, grounding, citations, provenance, and feedback.
Establish testing, evaluation, and release standards for data quality, retrieval, prompts.
Embed testing, monitoring, traceability, rollback, and governance controls in delivery pipelines.
Lead architecture reviews, align stakeholders, and guide technical teams.
What we value
The successful candidate will combine strategic thinking, enterprise architecture perspective, deep technical credibility, and a quality-and-risk mindset. The candidate must communicate complex architectural choices, test evidence, control requirements, risks, and trade-offs clearly to engineering teams, business stakeholders, governance forums, auditors, and senior technology leaders. We value pragmatic architecture, business-value alignment, secure-by-design and test-by-design practices, reuse, interoperability, measurable quality, transparent decision making, and continuously governed enterprise AI outcomes.
These skills will help you succeed in this role
Must-have skills
Strong experience in enterprise architecture, technical leadership, and working with cross-functional teams.
Strong data engineering expertise across ETL/ELT, batch and streaming pipelines, lakehouse or warehouse platforms, data quality, and DataOps.
Expertise in designing ontologies and semantic models and implementing end-to-end RAG and Graph RAG pipelines, including ingestion, chunking, embeddings, indexing, hybrid retrieval, semantic search, graph traversal, ranking, grounding, provenance, and evaluation.
Strong knowledge of Responsible AI, privacy, access control, model risk, auditability, regulatory compliance, and policy enforcement.
Experience delivering solutions in financial services or another regulated industry.
Nice-to-have skills
Exposure to modern data, streaming, workflow, monitoring, vector database, and enterprise search tools such as Snowflake, Databricks, Kafka, dbt, or Airflow would be useful.
Familiarity with AI platforms or frameworks such as Azure AI Foundry, Copilot Studio, Vertex AI, LangChain, LangGraph, LlamaIndex, Semantic Kernel, or MLflow would be beneficial.
Awareness of Model Context Protocol, connecting AI agents with tools, access controls, monitoring, and AI security would be helpful.
Experience establishing automated testing, quality gates, monitoring, traceability, release, and rollback controls within CI/CD and MLOps/LLMOps.
An understanding of cloud costs, capacity planning, and communicating architecture proposals to senior stakeholders would be useful.
Relevant certifications in architecture, cloud, data, graph, testing, security, governance, or Generative AI would be a plus.
Education & Preferred Qualifications
A bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, Data Engineering, Information Systems, or a related field is preferred; a master’s degree would be a plus.
Extensive technology experience, ideally including architecture, data or knowledge engineering, software or AI engineering, testing, governance, or enterprise platforms.
Experience providing architectural guidance and contributing to standards, roadmaps, governance, and key design decisions would be valuable.
Exposure to delivering secure, scalable, and well-governed data platforms, knowledge graphs, ontologies, RAG or Graph RAG services, Agentic AI, or Generative AI solutions would be beneficial.
The ability to work with senior stakeholders and bring business, architecture, engineering, testing, cloud, security, risk, and operations teams together would be helpful.
Relevant certifications in architecture, cloud, data, graph, testing, security, governance, or Generative AI would be a plus.
About State Street
Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.
We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
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