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Applied AI Researcher

Echo IT Solutions, Inc

  • Jersey City, NJ
  • 14 days ago
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    Skills

    • Amazon Web Services (AWS)unmatched
    • Artificial Intelligence (AI)unmatched
    • Bank Managementunmatched
    • Banking Servicesunmatched
    • Benchmarkingunmatched
    • Business Caseunmatched
    • Cloud Computingunmatched
    • Communication Skillsunmatched
    • Computational Linguisticsunmatched
    • Computer Scienceunmatched
    • Data Analysisunmatched
    • Data Recoveryunmatched
    • Data Setsunmatched
    • Develop Methodologiesunmatched
    • Documentationunmatched
    • Experiment Designunmatched
    • Financial Complianceunmatched
    • Information Retrievalunmatched
    • Know Your Customer (KYC)unmatched
    • Legalunmatched
    • Machine Toolunmatched
    • Mathematicsunmatched
    • Metricsunmatched
    • Model Validationunmatched
    • Natural Language Processing (NLP)unmatched
    • Open Sourceunmatched
    • Patentsunmatched
    • Performance Modelingunmatched
    • Prototypingunmatched
    • Publicationsunmatched
    • Research Skillsunmatched
    • Riskunmatched
    • Safety/Work Safetyunmatched
    • Scientific Researchunmatched
    • Statisticsunmatched
    • Technical Recruitingunmatched
    • Underwritingunmatched
    • Use Casesunmatched

    Description

    Applied AI Researcher
    New Jersey, Jersey city
    GenAI / NLP / Agentic AI / Applied Machine Learning

    LevelResearch-focused Individual Contributor
    Target / alternate titlesApplied Scientist; Research Scientist - NLP; AI Research Scientist; ML Researcher; NLP Scientist; GenAI Researcher; Applied ML Scientist
    Core keywordsapplied research, LLM, NLP, transformers, RAG, retrieval, model evaluation, experiments, robustness, embeddings, synthetic data, multimodal, PyTorch, Hugging Face, banking AI, AWS AI, AIRP
    Recruiter red flagsAcademic-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.


    Role purpose
    Bridge advanced AI research and practical enterprise use cases by validating models, methods, and prototypes that can become production-grade AIRP solutions. The role focuses on measurable business value, rigorous experimentation, model behavior, and safe translation of research into banking-relevant applications.
    Client-specific emphasis
    • Research must be grounded in enterprise business use cases, not generic AI experimentation.
    • Candidates should understand how model, retrieval, data, evaluation, latency, cost, and safety decisions affect production delivery on AIRP.
    • Cloud/AWS awareness is valuable because successful research outputs must be handed off to engineering teams building on AWS-hosted AIRP.
    Primary ownership
    • Applied research agenda for LLMs, NLP, RAG, evaluation, multimodal AI, and agentic workflows relevant to enterprise use cases.
    • Prototypes, experiments, benchmark design, model-selection recommendations, and production-readiness evidence.
    • Research-to-production handoff with AI engineering, AIRP platform, product, risk, and governance 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, 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 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, product, business, risk, and governance stakeholders.
    • Ability to translate research results into production requirements suitable for an AWS-hosted enterprise platform.
    Preferred experience
    • Research or applied science experience in banking, finance, compliance, risk, legal, operations, financial crime, sanctions, or enterprise knowledge systems.
    • Experience with AWS Bedrock, SageMaker, vector search, MLflow, Databricks, model evaluation tooling, or cloud-based experimentation environments.
    • Publications, patents, internal research contributions, open-source AI contributions, or prior research-to-production handoffs.
    • Familiarity with Responsible AI, model validation, privacy constraints, audit documentation, and regulated deployment environments.

    Regards,
    Sr. Technical Recruiter
    Shubham Chandra
    Shubham.c@echoitsol.com

    Numbers & Facts

    LocationJersey City, NJ

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