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Applied AI Researcher

Location: Jersey City, NJ (Hybrid – 4 Days Onsite)

Role Purpose & Key Responsibilities

The Applied AI Researcher will bridge cutting-edge AI research with practical enterprise business applications by designing, validating, and translating advanced AI techniques into production-ready capabilities for the AI Research & Innovation Platform (AIRP). This role focuses on delivering measurable business value through rigorous experimentation, model evaluation, prototype development, and the responsible adoption of Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), Agentic AI, Multimodal AI, and Applied Machine Learning.

The organization seeks researchers who can move beyond academic experimentation and develop solutions aligned with enterprise banking use cases. Research must consider how model architecture, retrieval strategies, embeddings, prompt design, data quality, latency, scalability, cost, explainability, safety, observability, security, and AWS cloud deployment impact successful production delivery on AIRP. The role will work closely with engineering teams to ensure research outputs can be operationalized within secure, scalable, and governed enterprise AI environments.

The successful candidate will lead the applied research agenda for enterprise AI by developing prototypes, experimentation frameworks, benchmark methodologies, model evaluations, production-readiness assessments, and research-to-production recommendations. Working closely with AI Engineers, Product Managers, Platform Engineering, Risk, Governance, and Business stakeholders, the Applied AI Researcher will transform innovative research into practical AI capabilities that support critical banking functions while adhering to Responsible AI, regulatory, and enterprise governance standards.

Key Responsibilities

• Conduct applied research in Large Language Models (LLMs), Generative AI, Natural Language Processing (NLP), Information Retrieval, Retrieval-Augmented Generation (RAG), Multimodal AI, Agentic AI, Synthetic Data Generation, Deep Learning, and Applied Machine Learning.
• Design and execute rigorous experiments to evaluate model accuracy, robustness, scalability, explainability, safety, latency, cost efficiency, interpretability, enterprise applicability, and production feasibility.
• Research, prototype, and validate AI solutions supporting enterprise business use cases including KYC, credit underwriting, governance tracking, Banker 360, Customer 360, pitch book generation, deal intelligence, financial crime detection, sanctions screening, document intelligence, and enterprise knowledge management.
• Develop comprehensive evaluation methodologies using golden datasets, benchmark frameworks, adversarial testing, offline evaluations, human review, business outcome metrics, Responsible AI metrics, and risk-based acceptance criteria.
• Evaluate and compare approaches including prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, vector search, fine-tuning, instruction tuning, model distillation, synthetic data generation, reinforcement learning techniques, and model adaptation to determine the most effective production strategy.
• Analyze and document model limitations, failure modes, hallucination patterns, bias risks, data assumptions, explainability gaps, safety concerns, performance boundaries, and deployment risks, providing actionable recommendations for regulated enterprise environments.
• Collaborate with AI Engineering and Platform teams to translate research prototypes into production-ready AIRP requirements, including AWS cloud architecture, scalability, latency optimization, observability, monitoring, security, infrastructure, and operational readiness.
• Continuously monitor advancements in AI research, LLM architectures, transformer models, multimodal AI, retrieval techniques, evaluation methodologies, and enterprise AI technologies, translating emerging innovations into practical recommendations for business adoption.
• Present research findings, experimental results, technical recommendations, and production guidance to engineering, product, business, risk, governance, compliance, and executive stakeholders through clear documentation and technical presentations.

Must-Have Candidate Profile

• Master's or Ph.D. preferred in Artificial Intelligence, Machine Learning, Computer Science, Statistics, Computational Linguistics, Mathematics, Data Science, or a related field.
• Strong foundation in Machine Learning, Deep Learning, Natural Language Processing (NLP), Transformer architectures, Information Retrieval, Large Language Models (LLMs), Generative AI, and Applied AI Research.
• Hands-on experience designing, evaluating, and optimizing LLMs, RAG systems, embeddings, vector databases, multimodal AI, model evaluation frameworks, and applied Generative AI solutions.
• Strong Python programming skills with frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, LangChain, LlamaIndex, or equivalent AI research libraries.
• Experience designing statistically rigorous experiments, benchmarking methodologies, evaluation pipelines, and communicating findings to technical and non-technical stakeholders.
• Ability to translate research outcomes into production-ready engineering requirements suitable for AWS-hosted enterprise AI platforms, balancing performance, scalability, security, governance, and operational constraints.

Preferred Experience

• Applied AI research experience within Banking, Financial Services, FinTech, Insurance, Risk Management, Compliance, Financial Crime, AML, Sanctions, Legal Technology, or Enterprise Knowledge Management.
• Experience with AWS Bedrock, Amazon SageMaker, MLflow, Databricks, vector search platforms, embedding services, cloud-native AI experimentation environments, and enterprise AI evaluation tooling.
• Publications in peer-reviewed conferences or journals, patents, open-source AI contributions, internal research initiatives, or demonstrated experience successfully transitioning research into production systems.
• Familiarity with Responsible AI, AI Governance, Model Validation, Privacy-by-Design, Enterprise Risk Management, Audit Documentation, Regulatory Compliance, and AI Safety within regulated industries.

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