20 days ago
USA (city not specified)Mid Level
H1B sponsor
Responsibilities
- Design, develop, deploy, and optimize AI, machine learning, NLP, document intelligence, and Generative AI solutions for disability insurance use cases.
- Translate business, actuarial, and claims objectives into analytical designs, AI/ML use cases, and production-ready components.
- Manage the full ML lifecycle from problem framing and data exploration through feature engineering, modeling, validation, deployment, monitoring, retraining, and improvement.
- Develop NLP and document intelligence pipelines for extracting, classifying, summarizing, and interpreting medical records, claim notes, correspondence, and other documents.
- Build Generative AI capabilities involving prompt engineering, embeddings, Retrieval-Augmented Generation, vector search, model evaluation, guardrails, and enterprise integration.
- Apply model validation, performance tuning, bias detection, explainability analysis, stability testing, and business impact assessment.
- Support MLOps activities including experiment tracking, model registries, deployment, monitoring, drift detection, and retraining.
- Communicate model outputs, limitations, risks, and recommendations to technical and non-technical stakeholders.
Requirements
- Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
- At least 4 years of hands-on experience in AI/ML, data science, or machine learning engineering.
- Experience 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.
- Experience supporting model deployment, productionization, monitoring, or MLOps is strongly preferred.
- Strong proficiency in Python, AI/ML libraries, SQL, and data manipulation.
- Knowledge of classification, regression, clustering, survival analysis, anomaly detection, feature engineering, model selection, and hyperparameter tuning.
- Hands-on experience with NLP, deep learning, document analytics, text classification, entity extraction, semantic search, summarization, information extraction, and document understanding.
- Practical exposure to Large Language Models, prompt engineering, embeddings, Retrieval-Augmented Generation, vector databases, LLM evaluation, hallucination control, and responsible AI guardrails.
- Working knowledge of MLOps, production ML practices, cloud platforms, data pipelines, APIs, relational databases, data modeling, and data architecture.
- Ability to evaluate model performance, explain outputs, identify bias or drift, and communicate limitations clearly.
- Understanding of responsible AI, model governance, data privacy, security, and compliance in regulated domains.
- Healthcare, insurance, disability claims, financial services, or other regulated-industry experience is preferred.
- Strong analytical thinking, engineering discipline, structured problem-solving, written and verbal communication, ownership, and cross-cultural collaboration skills.
Tech Stack
Apache AirflowAWSAzureDockerGitGoogle Cloud PlatformKubernetesLightGBMMLflowPythonPyTorchscikit-learnSQLTensorFlowXGBoost
Categories
About EXL
EXL provides data analytics, AI, and digital operations services for enterprises in insurance, healthcare, banking, and other regulated industries. It delivers consulting, managed services, and technology platforms to improve decision-making, risk management, and operational efficiency. Founded in 1999 and headquartered in New York, EXL is a public company listed on the NASDAQ (ticker: EXLS).