AI/ML Engineer

Technical Skillsets: 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. Solid 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 use cases 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. Working knowledge of MLOps and production ML practices, including experiment tracking, model registry, CI/CD for ML, model deployment, monitoring, drift detection, retraining, and tools such as MLflow, Airflow, Docker, Kubernetes, Git, or cloud-native ML platforms. 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. Exposure to cloud platforms such as Azure, AWS, or GCP and familiarity with scalable AI/ML solution development in cloud environments. Ability to evaluate model performance using appropriate metrics, explain model outputs, identify bias or drift, and communicate limitations clearly to technical and non-technical stakeholders. Understanding of responsible AI, model governance, data privacy, security, and compliance considerations in regulated domains. Exposure to group insurance, healthcare, disability insurance, claims analytics, or other regulated business domains will be preferred. Basic understanding of insurance data structures such as policies, insured members, coverages, claims, benefits, providers, medical records, and related entities will be an advantage. Strong problem-solving skills, ownership mindset, and ability to design scalable, reusable, and maintainable AI/ML solution components.