Data Scientist

Good exposure to Data science fundamentals besides developing and deploying models for language processing tasks. Improve the performance of existing models. Familiarity with language models like GPT and BERT, and the ability to fine-tune or adapt them for specific use cases. Familiarity with cloud services, ideally AWS ecosystem, and serverless architectures, to effectively deploy and manage AI solutions. Ability in creating APIs to integrate machine learning models into existing applications or services. Proficiency in programming languages like Python, JavaScript. Experience around NLP libraries and frameworks for text processing, sentiment analysis, named entity recognition, Intent classification, and other language-related tasks, such as scikit-learn, NumPy, NLTK, SpaCy. Work closely with LLM frameworks such as LangChain, OpenAI s APIs, Vector databases, and apply Prompt design techniques. Hands-on experience with some of the most popular ML frameworks (e.g., TensorFlow, PyTorch, Transformers, etc.). Awareness of the AI controllability, including privacy, bias, fairness, transparency, and the ability to implement AI systems responsibly and in compliance with legal and ethical guidelines. Willingness to stay updated with the latest developments in AI/ML and continuously learn new techniques and best practices. Experience working in insurance Domain. Experience working in Agile/Scrum methodologies and frameworks (JIRA), RESTful APIs, and DevOps principles.

Collaboration

Effective collaboration alongside developers and cross-functional teams. Ability to clearly communicate technical concepts to both technical and non-technical team members. Advocate for software engineering good practices (e.g., version control, documentation, test-driven development). Strong problem-solving skills to approach complex AI challenges and devise innovative solutions. Strong verbal and written communication skills in English. Ability to work independently and as part of a team.