Applied Researcher I (Multi-agent Systems, Knowledge Graphs/GraphRAG/Graph-of-Thought / GoT, MCP, LangGraph, Agent Protocols)
Core
Building scalable, state-of-the-art AI architectures and multi-agent solutions to transform the software development lifecycle and empower internal engineers.
Role type
Senior Applied Researcher (Multi-agent Systems & Knowledge Graphs)
Builds
Multi-agent solutions for design, code generation, system migration, and troubleshooting; production AI foundation models.
Domain
Banking / AI Software Engineering / Multi-agent Systems / Knowledge Graphs
Deliverable
production ML models | product features
Required skills
Deep learning model development, training optimization, self-supervised learning, robustness, explainability, RLHF, multi-agent system design, knowledge graph construction and reasoning, graph neural networks, LLM pre-training and fine-tuning, tool-use integration, memory management for agents, verifiable agent behavior, industrial-scale model deployment.
Preferred skills
Publications in deep learning theory or NLP venues (ACL, NAACL, Neurips, ICML), experience training LLMs from scratch (10B+ parameters), expertise in agentic AI research, graph database implementation (Neo4j, JanusGraph).
Technologies
Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, LangGraph, Neo4j, JanusGraph
Responsibilities
Partner with cross-functional teams to deliver AI-powered products; leverage advanced stacks to reveal insights in large data volumes; build AI foundation models through design, training, evaluation, and implementation; engage in high-impact applied research to push AI developments into customer experiences.
Seniority
Senior, hands-on IC with research leadership