Applied AI Researcher

Title: Applied AI Researcher

Location: Jersey City, NJ(Hybrid)

Type: Contract

Description:

• Bridge advanced AI research and practical enterprise use cases by validating models, methods, and prototypes that can become production-grade solutions. The role focuses on measurable business value, rigorous experimentation, model behavior, and safe translation of research into banking-relevant applications.
• Primary ownership
• Applied research agenda for LLMs, NLP, RAG, evaluation, multimodal AI, and agentic workflows.
• Prototypes, experiments, benchmark design, and model selection recommendations.
• Research-to-production handoff with AI engineering and platform teams.
• Key responsibilities
• Conduct applied research in LLMs, GenAI, NLP, information retrieval, multimodal AI, synthetic data, and agentic AI.
• Design experiments to evaluate model performance, robustness, safety, scalability, interpretability, and enterprise usefulness.
• Prototype AI solutions for use cases such as document intelligence, financial analysis, compliance support, knowledge retrieval, and operational automation.
• Develop evaluation methodologies using golden datasets, adversarial testing, offline benchmarks, human review, and business outcome metrics.
• Assess prompt optimization, RAG, fine-tuning, instruction tuning, synthetic data generation, distillation, and model adaptation techniques.
• Collaborate with engineers to convert prototypes into production-ready systems with clear requirements, limitations, and acceptance criteria.
• Track emerging AI research and translate relevant advances into practical recommendations for the enterprise.
• Produce internal research papers, technical notes, implementation guides, and thought-leadership materials.

Must-have candidate profile:

• Advanced degree preferred, usually MS or PhD in AI, ML, computer science, statistics, computational linguistics, mathematics, or related field.
• Strong foundation in machine learning, deep learning, NLP, transformers, information retrieval, and generative AI.
• Hands-on experience with LLMs, embeddings, RAG, model evaluation, and applied GenAI experimentation.
• Python skills with PyTorch, TensorFlow, Hugging Face, scikit-learn, or equivalent research frameworks.
• Ability to design rigorous experiments and communicate findings to technical and business stakeholders.
• Preferred experience
• Research or applied science experience in banking, finance, compliance, risk, legal, operations, or enterprise knowledge systems.
• Publications, patents, internal research contributions, or open-source AI contributions.
• Familiarity with Responsible AI, model validation, privacy constraints, and regulated deployment environments.
• Initial screening questions
• What research idea did you convert into a prototype or production capability?
• How do you design an evaluation harness for an LLM-based banking use case?
• How do you determine whether fine-tuning, RAG, or prompting is the right approach?
• What failure modes did you discover and how did you mitigate them?
• How do you communicate model limitations to non-research stakeholders?