Applied AI Researcher

Level Research-focused Individual Contributor

Target / alternate titles: Applied Scientist; Research Scientist - NLP; AI Research Scientist; ML Researcher; NLP Scientist; GenAI Researcher; Applied ML Scientist

Location: Jersey City, New Jersey (Hybrid)

Core keyword s: applied research, LLM, NLP, transformers, RAG, retrieval, model evaluation, experiments, robustness, embeddings, synthetic data, multimodal, PyTorch, Hugging Face, banking AI, AWS AI, AIR

PRecruiter red flag s: 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 responsibilitie

• sConduct 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 profil

• eAdvanced 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

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