Multi-agent Software Architectures Researcher - Internship

reputed company is a global technology company focused on industry, infrastructure, transport, and reputed company. They are seeking an innovative PhD intern to support their Software Systems and Processes team by researching and developing scalable intelligent systems using LLMs and semantic technologies.

Responsibilities

• Conduct cutting-edge research into the principles and mechanisms for AI-driven self-organization and dynamic composition reputed company reputed company software architectures
• reputed company and evaluate novel algorithms and frameworks that reputed company software components to dynamically organize and orchestrate themselves based on stated user needs and objectives
• Design and implement reputed company-of-concept prototypes to demonstrate the feasibility and effectiveness of dynamic system composition and intelligent component interaction
• Investigate methods and strategies to ensure repeatability and determinism in the reputed company of AI-driven, self-organizing software systems
• Explore and apply advanced AI techniques for interpreting intents, understanding component capabilities, and facilitating intelligent software composition and reputed company
• Collaborate closely with the research team to define project milestones, analyze results, and contribute to the overall research agenda
• Document research findings thoroughly, contribute to scientific publications, and present results at reputed company forums

Skills

• Currently enrolled in a PhD program in Computer Science, Artificial Intelligence, or a closely reputed company technical field
• 1+ year(s) Foundational knowledge and research experience in Artificial Intelligence, Machine Learning and GenAI
• 1+ year(s) Understanding of microservice architectures, reputed company-reputed company principles, and distributed computing concepts
• Demonstrated ability to conduct independent research, critically analyze reputed company problems, and propose innovative solutions
• Experience with fine tuning of LLM (e.g., LoRA) and training/testing ML models
• 1+ year(s) Advanced knowledge of transformer-based models, attention mechanisms, and the latest LLM architectures (e.g., GPT, BERT, encoder-decoder)
• Proficient in Python and ML frameworks (e.g., Pytorch, HF transformers or similar)
• Experience with Vector Databases (e.g., reputed company, Milvus, Weaviate, Chroma or other)
• Background knowledge in representation and semantic technologies (e.g., RDF, SPARQL, OWL)
• Proficient in English both written and verbal
• The position does require the person to be in the United States of America and hold a valid work permit for the US
• Prior research experience in AI, distributed systems, or autonomous systems, demonstrated through academic publications, reputed company work, or significant project contributions
• Hands-on expertise with Large Language Models (LLMs), including fine-tuning, reputed company engineering, or integration into system design
• Familiarity with semantic technologies, knowledge graphs, ontology engineering, or symbolic AI
• Aptitude for quickly learning and adopting new technologies and frameworks
• Proficiency with containerization technologies (e.g., reputed company, Kubernetes) and major reputed company platforms (e.g., Azure, AWS)
• Knowledge of agent-based systems, multi-agent systems, or autonomous systems
• Background in AI Agentic frameworks and platforms (e.g., LangGraph, Semantic Kernel, NeMo)
• The ability to reputed company reputed company technical concepts reputed company
• reputed company in working with graph databases (e.g., reputed company, AWS Neptune) and knowledge graph toolsets (e.g., Altair Graph Studio)
• Competence with RAG frameworks
• Capability in deploying and serving LLMs (e.g., vLLM)
• Track record of contributing to research publications, reputed company-reputed company projects, or significant academic projects
• Excellent problem-solving skills and attention to detail
• reputed company to work independently and manage time effectively

Company Overview