AI Architect

EXL

. Design, develop, and deploy AI, Machine Learning, and Generative AI solutions addressing business problems across the disability insurance value chain.

Posted 10/22/2026full-timeNew Jersey • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying AI and Machine Learning solutions, with a strong focus on NLP and Generative AI techniques. Proficient in the full ML lifecycle, including model development, validation, and production practices, while ensuring compliance and responsible AI standards.

Highest-signal resume keywords

AI/ML Solution DevelopmentNLP and Document IntelligenceGenerative AI TechniquesPython and AI/ML LibrariesMLOps and Production Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningNatural Language ProcessingFeature EngineeringModel ValidationData ManipulationSQLData ArchitecturePrompt EngineeringHyperparameter Tuning

Soft Skills

Problem-SolvingCommunicationCollaboration

Tools & Technologies

Scikit-learnTensorFlowPyTorchHugging FaceLangChainAzureAWSGCP

Industry Keywords

Responsible AIData PrivacyModel GovernanceComplianceBusiness Impact Assessment

Tech Stack

Tools & technologies

AWSAzureGoogle Cloud PlatformPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, develop, and deploy AI, Machine Learning, and Generative AI solutions addressing business problems across the disability insurance value chain.
Translate business, actuarial, and claims objectives into practical AI/ML use cases, analytical designs, and production-ready solution components.
Work across the full ML lifecycle, including problem framing, data exploration, feature engineering, model development, validation, deployment support, monitoring, retraining, and continuous improvement.
Build and optimize supervised, unsupervised, deep learning, NLP, and Generative AI models using structured and unstructured data.
Develop NLP and document intelligence pipelines for extracting, classifying, summarizing, and interpreting medical records, claim notes, correspondence, and other documents.
Develop Generative AI solutions using prompt engineering, embeddings, Retrieval-Augmented Generation, vector search, model evaluation, guardrails, and enterprise application integration.
Apply model validation practices including performance tuning, bias detection, explainability analysis, stability testing, and business impact assessment.

Requirements

What you’ll need

Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
4+ years of hands-on experience in AI/ML, data science, or machine learning engineering.
Experience building analytical or AI-enabled solutions supporting measurable business outcomes.
Experience working with structured and unstructured data and applying machine learning, NLP, deep learning, or Generative AI techniques.
Strong hands-on proficiency in Python and common AI/ML libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, Hugging Face, and LangChain or similar frameworks.
Solid understanding of classification, regression, clustering, survival analysis, anomaly detection, feature engineering, model selection, and hyperparameter tuning.
Hands-on experience with NLP, deep learning, and document analytics use cases.
Practical exposure to Generative AI and Large Language Models, including prompt engineering, embeddings, Retrieval-Augmented Generation, vector databases, LLM evaluation, hallucination control, and responsible AI guardrails.
Working knowledge of MLOps and production ML practices.
Strong SQL and data manipulation skills.
Understanding of relational databases, data modeling, data pipelines, APIs, and data architecture fundamentals.
Exposure to Azure, AWS, or GCP.
Understanding of responsible AI, model governance, data privacy, security, and compliance considerations in regulated domains.
Strong problem-solving skills and ability to design scalable, reusable, and maintainable AI/ML solution components.
Strong written and verbal communication skills and ability to explain analytical findings to technical and business stakeholders.
Ability to collaborate across cultures and geographies.

Benefits

Comp & perks

Hybrid work arrangement
Full-time employment