Applied AI Researcher

Level: Research-focused Individual ContributorTarget / alternate titles: Applied Scientist; Research Scientist - NLP; AI Research Scientist; ML Researcher; NLP Scientist; GenAI Researcher; Applied ML ScientistLocation: Jersey City, New Jersey (Hybrid)Core keywords: applied research, LLM, NLP, transformers, RAG, retrieval, model evaluation, experiments, robustness, embeddings, synthetic data, multimodal, PyTorch, Hugging Face, banking AI, AWS AI, AIRPRecruiter red flags: Academic-only profile with no applied delivery; weak experimental design; cannot translate research into AIRP-ready business or engineering requirements; no awareness of regulated data constraints.Key responsibilitiesConduct 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, enterprise usefulness, and production feasibility.Prototype AI solutions for KYC, credit underwriting, governance tracking, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening.Develop evaluation methodologies using golden datasets, adversarial testing, offline benchmarks, human review, business outcome metrics, and risk-specific acceptance criteria.Assess prompt optimization, RAG, fine-tuning, instruction tuning, synthetic data generation, distillation, and model adaptation techniques.Document model limitations, data assumptions, hallucination patterns, bias risks, performance boundaries, and control recommendations for regulated deployment.Collaborate with engineers to convert prototypes into production-ready AIRP requirements, including latency, cost, observability, security, and AWS/cloud deployment considerations.Track emerging AI research and translate relevant advances into practical recommendations for the enterprise.Must-have candidate profileAdvanced 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, product, business, risk, and governance stakeholders.Ability to translate research results into production requirements suitable for an AWS-hosted enterprise platform.