AI Engineer
Architecting multi-agent systems
that reason, collaborate, and ship.
Thesis intern at Ericsson, designing a multi-agent reasoning framework that integrates ontology-driven knowledge graphs for reliable, explainable decision-making in enterprise settings. Building specialized agent teams that ingest, validate, and reason over heterogeneous knowledge sources in Neo4j.
I'm an AI Engineer specializing in multi-agent systems, knowledge graphs, and LLM pipelines — with hands-on experience at Ericsson and Scale AI.
I care about the gap between a working demo and a trustworthy system — clean agent orchestration, explainable outputs, and architectures that hold up when real data hits them.
Designing and evaluating a framework that integrates ontology-driven knowledge graphs with multi-agent reasoning for reliable decision-making in enterprise settings. Built specialized agent teams (ingestion, validation, conflict detection, planning, auditing) that collaborate to reason over a Neo4j knowledge graph and reduce human intervention. Evaluated the system on an infrastructure planning case study in terms of reliability, explainability, and automation.
Prototyped a LangGraph-based multi-agent framework with role-like orchestration for dynamic dialogue routing. Integrated multi-source data (logs, documents, KPIs) to support RCA-like troubleshooting. Applied RAG-based reasoning with query rewriting, embedding retrieval, and document scoring to improve explainability. Delivered a prototype with FastAPI + Gradio WebUI supporting user registration, session management, and historical conversation loading.
Led evaluation and training of LLMs across annotation tasks including model ranking, instruction tuning, and prompt refinement. Audited 100+ tasks weekly maintaining >95% quality compliance while mentoring junior annotators. Developed expertise in prompt engineering, LLM reasoning strategies, and applied generative AI. Achieved Coding T2 Certification in advanced LLM reasoning.
Specializing in multi-agent AI orchestration and distributed reasoning.
Joint degree programme with KTH. Coursework at Aalto covering advanced topics in distributed systems and AI-driven network intelligence.
Dual degree in Robotics Engineering and Computer Science. Built strong foundations in systems programming, machine learning, and autonomous systems.
LangGraph-based multi-agent framework with role-like orchestration for dynamic dialogue routing. Integrates multi-source data (logs, documents, KPIs) for RCA-like troubleshooting with RAG-based explainability.
Designed and refined evaluation workflows for large language models at Scale AI, focusing on coherence, factual accuracy, and collaborative reasoning. Audited 100+ tasks weekly at >95% quality compliance.
Designed and evaluated multi-agent architectures for telecom applications, focusing on structured communication protocols, adaptive task allocation, and contextual collaboration between specialized agents.
Open to full-time roles and internship opportunities in AI, multi-agent systems, and LLM engineering.
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