AI Architect

EXL•New Jersey, NJ

About The Position

Designs, develops, and deploys AI, machine learning, and Generative AI solutions for business problems across the disability insurance value chain. Potential applications include claims segmentation, fraud detection, risk scoring, duration modeling, outcome prediction, and next-best-action recommendations. The role translates business, actuarial, and claims objectives into practical AI/ML use cases, analytical designs, and production-ready solution components. The AI Architect works across the full machine learning lifecycle, from problem framing and data exploration through feature engineering, model development, validation, deployment support, monitoring, retraining, and continuous improvement. The role builds and optimizes supervised, unsupervised, deep learning, NLP, and Generative AI models using structured and unstructured data, including claims history, medical records, claim notes, policy data, and operational data. It also develops NLP and document intelligence pipelines and supports Generative AI solutions involving prompt engineering, embeddings, Retrieval-Augmented Generation, vector search, model evaluation, guardrails, and enterprise integration. The role applies model validation practices such as performance tuning, bias detection, explainability analysis, stability testing, and business impact assessment. It requires collaboration with technical and business stakeholders and attention to responsible AI, model governance, privacy, security, and compliance in regulated domains.

Requirements

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, building analytical or AI-enabled solutions that support measurable business outcomes.
Experience working with structured and unstructured data and applying machine learning, NLP, deep learning, or Generative AI techniques to practical business problems.
Strong hands-on proficiency in Python and common AI/ML libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, Hugging Face, LangChain, or similar frameworks.
Understanding of machine learning and statistical modeling techniques, including classification, regression, clustering, survival analysis, anomaly detection, feature engineering, model selection, and hyperparameter tuning.
Hands-on experience with NLP, deep learning, and document analytics, such as text classification, entity extraction, semantic search, summarization, information extraction, and document understanding.
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.
Strong SQL and data manipulation skills, with the ability to work with large structured, semi-structured, and unstructured datasets.
Good understanding of relational databases, data modeling concepts, data pipelines, APIs, and data architecture fundamentals.
Ability to evaluate model performance using appropriate metrics, explain model outputs, identify bias or drift, and communicate limitations clearly.
Understanding of responsible AI, model governance, data privacy, security, and compliance considerations in regulated domains.
Strong analytical thinking, engineering discipline, structured problem-solving, ownership, and communication skills.
Ability to work in a fast-paced, continuously evolving environment and take ownership of challenging AI/ML delivery tasks.
Ability to collaborate effectively across cultures and work with clients and teams across geographies.

Nice To Haves

Working knowledge of MLOps and production ML practices, including experiment tracking, model registry, CI/CD for ML, deployment, monitoring, drift detection, and retraining.
Familiarity with tools such as MLflow, Airflow, Docker, Kubernetes, Git, or cloud-native ML platforms.
Exposure to cloud platforms such as Azure, AWS, or GCP and familiarity with scalable AI/ML solution development in cloud environments.
Experience supporting model deployment, productionization, monitoring, or MLOps activities.
Exposure to group insurance, healthcare, disability insurance, claims analytics, financial services, or other regulated domains.
Basic understanding of insurance data structures such as policies, insured members, coverages, claims, benefits, providers, and medical records.

Responsibilities

Design, develop, and deploy AI, machine learning, and Generative AI solutions for business problems across the disability insurance value chain.
Translate business, actuarial, and claims objectives into AI/ML use cases, analytical designs, and production-ready solution components.
Work across the 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 to extract, classify, summarize, and interpret information from medical records, claim notes, correspondence, and other documents.
Support Generative AI development, including 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.
Evaluate model performance, explain model outputs, identify bias or drift, and clearly communicate limitations.
Design scalable, reusable, and maintainable AI/ML solution components.
Collaborate with clients and teams across geographies and communicate analytical findings, risks, and recommendations to technical and business stakeholders.