Artificial Intelligence (AI) Research Scientist On-Call

About the role

Reporting to the DINA Lead, the Artificial Intelligence (AI) Research Scientist On-Call position will play a key role in the development and advancement of an AI-driven Decision Support System (DSS). This position is responsible for designing, developing, and integrating advanced AI technologies, including real-time data processing, retrieval-augmented generation (RAG), Speech-to-Text (STT), and Text-to-Speech (TTS) models and APIs to enhance decision-making capabilities across diverse industries. The AI Research Scientist will collaborate with a multidisciplinary team of Ph.D.-level AI experts specializing in multi-cloud architectures (Azure, AWS, and Google Cloud Platform), natural language processing (NLP) pipelines, scalable knowledge graphs, and AI-driven agent frameworks. The role requires conducting applied and translational research, developing innovative AI solutions, publishing and disseminating findings where appropriate, and contributing to the deployment of scalable, production-ready systems. In addition to technical contributions, the AI Research Scientist will provide mentorship and guidance to student team members, supporting their professional development, research engagement, and technical skill development while fostering a collaborative learning environment.

Responsibilities

• Research, develop, validate, and deploy AI models, algorithms, and data pipelines for processing large-scale structured and unstructured datasets, leveraging natural language processing (NLP), knowledge graphs, and semantic search technologies.
• Design, implement, and optimize secure, scalable, and high-performance AI solutions across multi-cloud environments, including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).
• Enhance and maintain retrieval-augmented generation (RAG) frameworks to improve knowledge retrieval, contextual understanding, and recommendation accuracy.
• Develop and optimize integrations with Neo4j, vector databases, and related technologies to support dynamic knowledge graph applications and advanced data relationships.
• Lead the research, development, fine-tuning, evaluation, and benchmarking of large language models (LLMs) to support automated decision-making and intelligent recommendations.
• Design, build, and deploy AI-powered agents capable of interpreting complex queries, executing automated workflows, and delivering context-aware insights and recommendations.
• Develop, integrate, and maintain Speech-to-Text (STT) and Text-to-Speech (TTS) models, services, and APIs to enable advanced voice-enabled AI interactions.
• Design and implement secure AI workflows, data governance practices, and privacy controls to support the handling of sensitive and enterprise-level data.
• Collaborate with software engineers, data scientists, and cross-functional stakeholders to improve automated document ingestion, classification, information extraction, and data management processes.
• Conduct applied research, evaluate emerging AI technologies, author technical reports and scholarly publications as appropriate, and identify opportunities for innovation and continuous improvement.
• Mentor student researchers, graduate assistants, and junior team members by providing technical guidance, research mentoring, and professional development support.

Requirements

• Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, Computer Engineering, Electrical Engineering, Natural Language Processing (NLP), or a closely related field.
• Minimum of five (5) years of experience in AI/ML research, development, and deployment, with a strong emphasis on enterprise-scale and multi-cloud AI solutions.
• Advanced proficiency in Python, PyTorch, TensorFlow, LangChain, DSPy, or equivalent AI development frameworks.
• Expertise in natural language processing (NLP), retrieval-augmented generation (RAG), semantic search, knowledge graphs, and graph-based reasoning systems.
• Extensive experience with Neo4j, vector databases, vector search technologies, and scalable knowledge management architectures.
• Proven track record of designing, developing, and deploying AI-powered agents, autonomous systems, and workflow automation solutions.
• Deep expertise in Speech-to-Text (STT) and Text-to-Speech (TTS) technologies, including model development, fine-tuning, optimization, and API integration.
• Strong understanding of federated AI architectures, data governance, cybersecurity principles, privacy regulations, and compliance requirements for AI systems.
• Experience designing and implementing AI solutions across major cloud platforms, including Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).
• Experience with model evaluation, AI safety, explainability, and responsible AI principles.
• Demonstrated ability to collaborate effectively within interdisciplinary research and development teams, while providing mentorship and technical guidance to student researchers and junior team members.
• Strong analytical, problem-solving, communication, and project management skills, with the ability to manage multiple priorities in a dynamic research environment.

Qualifications

• Five to ten (5–10) years of progressively responsible experience in AI/ML research, development, and deployment, or an equivalent combination of education, research, and professional experience.
• Experience leading or contributing to interdisciplinary research and development initiatives involving advanced AI technologies is highly desirable.
• Preferred: experience designing, developing, and deploying enterprise-scale AI applications and intelligent decision support systems.
• Preferred: robust research background demonstrated through peer-reviewed publications, conference presentations, patents, or other scholarly contributions in artificial intelligence, machine learning, natural language processing (NLP), knowledge graphs, or AI-driven decision support systems.
• Preferred: experience with transformer-based language models and modern AI frameworks, such as BERT, GPT, STORM, DSPy, or similar architectures.
• Preferred: experience with Claude, Gemini, Llama, Mistral, and related open-source or commercial foundation model ecosystems.
• Preferred: hands-on experience in retrieval-augmented generation (RAG), agentic AI systems, vector databases, large language model (LLM) fine-tuning, MLOps practices, model monitoring, and the deployment of AI solutions in production environments.
• Preferred: experience securing external research funding, contributing to grant proposals, or participating in sponsored research programs.

Skills

• Natural language processing (NLP)
• Retrieval-augmented generation (RAG)
• Semantic search
• Knowledge graphs and graph-based reasoning
• Speech-to-Text (STT) and Text-to-Speech (TTS) model development and API integration