Internship - IT & Architecture Brain

During this internship, you will design and develop a working prototype that serves as a common memory and context layer for IT and Architecture, supporting several AI-driven use cases through a unified system-of-systems model.

As a leading global reinsurer, SCOR offers its clients a diversified and innovative range of reinsurance and insurance solutions and services to control and manage risk. Applying "The Art & Science of Risk," SCOR uses its industry-recognized expertise and cutting-edge financial solutions to serve its clients and contribute to the welfare and resilience of society in around 160 countries worldwide.

SCOR means engaging with some of the best minds in the industry

• actuaries, data scientists, underwriters, risk modelers, engineers, and many others
• as we work together to find solutions to pressing challenges facing societies.

As an international company, our common culture is defined by "The SCOR Way." Serving both to build momentum that drives the Group forward and as a compass to guide our actions and choices, The SCOR Way is anchored by five core values, reflecting the input of employees at all levels of the Group. We care about clients, people, and societies. We perform with integrity. We act with courage. We encourage open minds. And we thrive through collaboration.

SCOR supports inclusion and the diversity of talents, and all positions are open to people with disabilities.

The intern will design and build a proof-of-concept "IT & Architecture Brain," structured not as one monolithic assistant but a set of specialized agents, each responsible for one activity, each accumulating its own working context and knowledge over time, all drawing on a shared transverse knowledge layer so they stay consistent with one another.

• Architecture knowledge agent - answers "what exists, why was it built this way, and how does it connect" by retrieving and reasoning over the documentations, ADRs.
• Change impact agent
• reasons about blast radius before a change ("if we deprecate this API/service, what breaks and who owns it"), useful for architecture and change advisory boards.
• Incident & runbook agent
• holds persistent memory across past incidents and postmortems, surfacing "this looks like the outage from [date], here's what fixed it" instead of starting from zero each time.
• Governance & compliance agent - checks new designs/ADRs against existing standards, reference architectures, and prior decisions, flagging inconsistencies or duplicated capabilities across teams. Rather than four disconnected tools, the goal is to demonstrate that one well-structured mesh, sitting on one shared knowledge layer, can power all four agents.

1)Discovery : Review of enterprise brain tooling approaches; inventory of available sources; scope confirmation 2)Shared layer & mesh design : Define the shared data model (systems, dependencies, decisions, incidents); define each agent's scope, context, and memory model; sketch the harness (routing, handoff, access control); design evaluation metrics

• Core build EA knowledge agent : Ingest system catalog, ADRs ; build retrieval + reasoning for agent 1 ; build a minimal harness (single-agent routing, logging) 4)Mesh extension : Add one or two more agents (change impact, incident memory, or governance); extend the harness to multi-agent routing and handoff; wire agents to the shared layer and their own memory 5)Reliability & evaluation : Consistency checks across agents, access control, audit logging, fallback/escalation behavior, benchmark against stateless baseline, validation with architects/engineers

- Documentation : Final report, demo, recommendations for scaling the mesh to more agents/teams

Key duties and responsibilities

• A working prototype of the agent mesh (codebase + demo): at least two agents, a shared knowledge layer, and a harness coordinating them
• An architecture document describing the shared data model, each agent's scope/context/memory, and the harness's routing and access-control logic
• An evaluation report covering per-agent accuracy
• A final presentation with recommendations on extending the mesh to additional agents, data sources, or teams

Required experience & competencies

• Programming skills (Python preferred)
• Familiarity with LLMs and how they're used
• Basic understanding of information retrieval concepts (embeddings, vector search)
• Interest in software/enterprise architecture concepts (system design, dependencies, documentation practices)
• Comfort working with ambiguous, evolving requirements and iterating quickly

• Experience with RAG frameworks like LangChain
• Exposure to graph databases or knowledge graphs (Neo4j, etc)
• Familiarity with multi-agent frameworks, orchestration patterns
• Interest in distributed systems reliability concepts (circuit breakers, fallback, observability) applied to AI agents