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Remote senior ML/GenAI role with broad skills attracts moderate applicant competition despite smaller company brand.
Core ML/GenAI skills are transferable, though insurance domain experience improves fit.
Explicit 7–15 years requirement plus specific GenAI, MLOps and ML tool mandates tighten shortlisting considerably.
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Design, develop, and deploy scalable AI/ML models and Generative AI solutions (incl. LLMs) for large-scale structured and unstructured data.
Drive AI solutions in insurance domain including claims anomaly/fraud detection, underwriting support, and document processing using OCR + NLP pipelines.
Lead end-to-end MLOps including data pipelines, feature engineering, model deployment, monitoring, and collaboration with cross-functional teams.
7 to 15 years of experience in AI/ML engineering including ML, deep learning, and Generative AI.
Proficiency in Python and ML frameworks (Scikit-learn, XGBoost, PyTorch/TensorFlow) and experience with LLMs (OpenAI, Hugging Face, Claude, etc.).
Experience with insurance domain use cases such as claims processing, fraud detection, underwriting analytics is required.
Familiarity with cloud platforms (Azure, AWS, or GCP), data pipeline tools (Spark, Airflow), MLOps (Docker, CI/CD, FastAPI), and version control.
Experienced in building AI solutions for insurance industry focusing on claims, underwriting, and fraud detection use cases.
Strong hands-on operational expertise across traditional ML, Generative AI, MLOps, and scalable data engineering.
Able to lead end-to-end AI project lifecycle from model development to deployment and monitoring in a hybrid/remote setting with quarterly travel to Madurai.