Key Responsibilities 
<ul>
<li style="margin-left:0px;">Develop and Fine-tune LLMs: Train and adapt large language models for domain-specific applications such as policy summarization, claims document understanding, and automated customer responses. </li>
<li style="margin-left:0px;">Generative AI Solutions: Build solutions leveraging text, image, and structured data to automate underwriting notes, claim justifications, and internal knowledge retrieval. </li>
<li style="margin-left:0px;">Data Engineering &amp; Model Pipeline: Work with data engineers to prepare, clean, and label unstructured insurance data (PDFs, scanned forms, emails, voice transcripts). Assist in collecting, cleaning, and preprocessing data for analysis and modelling. </li>
<li style="margin-left:0px;">Participate in cross-functional discussions and strategic decision-making related to AI directions and product architectures. </li>
<li style="margin-left:0px;">Prompt Engineering: Design, optimize, and evaluate prompts for LLM-based tools to ensure accuracy, compliance, and consistency in outputs. </li>
<li style="margin-left:0px;">Integration with Business Systems: Deploy AI models into production environments integrated with CRM, claims management, and policy administration systems. </li>
<li style="margin-left:0px;">Responsible AI and Governance: Ensure fairness, transparency, and compliance with data privacy and insurance regulations when using GenAI models. </li>
<li style="margin-left:0px;">Cross-functional Collaboration: Work closely with product management, underwriters, claims managers, actuaries, and customer service teams to identify automation and augmentation opportunities. </li>
<li style="margin-left:0px;">Innovation and Research: Stay current on emerging GenAI technologies (RAG, fine-tuning, multi modal models, vector databases) and pilot them within business contexts. </li>
</ul>
Required Qualifications 
<ul>
<li style="margin-left:0px;">Master’s or bachelor’s degree in computer science, Data Science, Statistics, AI, or related field. </li>
<li style="margin-left:0px;">2–5 years of experience in data science, NLP, or AI roles — with at least 1 year hands-on experience with GenAI / LLM-based solutions and production-grade AI/ML applications </li>
<li style="margin-left:0px;">Strong programming experience in Python, using frameworks such as PyTorch, TensorFlow, LangChain, Hugging Face, or OpenAI APIs, FastAPI, Git, Linux, and Docker. </li>
<li style="margin-left:0px;">Proficiency in SQL, data wrangling, and cloud environments (AWS, Azure, or GCP). </li>
<li style="margin-left:0px;">Strong grounding in machine learning, transformer architectures, and vector database technologies </li>
<li style="margin-left:0px;">Demonstrated experience with document understanding, summarization, Q&amp;A, or conversational AI projects. </li>
</ul>
Preferred / Nice-to-Have 
<ul>
<li style="margin-left:0px;">Domain expertise in insurance, health tech, or financial services. </li>
<li style="margin-left:0px;">Experience deploying AI applications in secure, regulated environments. </li>
<li style="margin-left:0px;">Familiarity with RAG pipelines, LLMOps, or model evaluation frameworks. </li>
<li style="margin-left:0px;">Understanding of OCR, knowledge graphs, and semantic search. </li>
<li style="margin-left:0px;">Publications, hackathon wins, or open-source GenAI project contributions. </li>
</ul>
Key Metrics of Success 
<ul>
<li style="margin-left:0px;">Accuracy, efficiency, and ROI of deployed GenAI models. </li>
<li style="margin-left:0px;">Reduction in manual effort for underwriting/claims/customer service teams. </li>
<li style="margin-left:0px;">Adoption and satisfaction levels of AI tools among internal users. </li>
<li style="margin-left:0px;">Compliance and governance adherence in GenAI outputs. </li>
</ul>

Data Scientist

Key Responsibilities Develop and Fine-tune LLMs: Train and adapt large language models for domain-specific applications such as policy summarization, claims document understanding, and automated customer responses. Generative AI Solutions: Build solutions leveraging text, image, and structured data to automate underwriting notes, claim justifications, and internal knowledge retrieval. Data Engineering &amp; Model Pipeline: Work with data engineers to prepare, clean, and label unstructured insurance data (PDFs, scanned forms, emails, voice transcripts). Assist in collecting, cleaning, and preprocessing data for analysis and modelling. Participate in cross-functional discussions and strategic decision-making related to AI directions and product architectures. Prompt Engineering: Design, optimize, and evaluate prompts for LLM-based tools to ensure accuracy, compliance, and consistency in outputs. Integration with Business Systems: Deploy AI models into production environments integrated with CRM, claims management, and policy administration systems. Responsible AI and Governance: Ensure fairness, transparency, and compliance with data privacy and insurance regulations when using GenAI models. Cross-functional Collaboration: Work closely with product management, underwriters, claims managers, actuaries, and customer service teams to identify automation and augmentation opportunities. Innovation and Research: Stay current on emerging GenAI technologies (RAG, fine-tuning, multi modal models, vector databases) and pilot them within business contexts. Required Qualifications Master’s or bachelor’s degree in computer science, Data Science, Statistics, AI, or related field. 2–5 years of experience in data science, NLP, or AI roles — with at least 1 year hands-on experience with GenAI / LLM-based solutions and production-grade AI/ML applications Strong programming experience in Python, using frameworks such as PyTorch, TensorFlow, LangChain, Hugging Face, or OpenAI APIs, FastAPI, Git, Linux, and Docker. Proficiency in SQL, data wrangling, and cloud environments (AWS, Azure, or GCP). Strong grounding in machine learning, transformer architectures, and vector database technologies Demonstrated experience with document understanding, summarization, Q&amp;A, or conversational AI projects. Preferred / Nice-to-Have Domain expertise in insurance, health tech, or financial services. Experience deploying AI applications in secure, regulated environments. Familiarity with RAG pipelines, LLMOps, or model evaluation frameworks. Understanding of OCR, knowledge graphs, and semantic search. Publications, hackathon wins, or open-source GenAI project contributions. Key Metrics of Success Accuracy, efficiency, and ROI of deployed GenAI models. Reduction in manual effort for underwriting/claims/customer service teams. Adoption and satisfaction levels of AI tools among internal users. Compliance and governance adherence in GenAI outputs.

Data Scientist in Hyderabad (UID-16363)

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