Actuarial Data Manager leading and mentoring data analysts in health sector financial objectives and analyses. Overseeing data quality and assisting with financial forecasting and reserving.
About the role
- Data Scientist at Yum India refining AI systems and conversational flows for Taco Bell's brand voice. Collaborate in model tuning and cross-functional teams to enhance user experience.
Responsibilities
- Author and refine prompt instructions, chaining logic, and fallback strategies to ensure seamless conversation flows.
- Design and test multi-turn conversation flows, aligning them with the Taco Bell brand voice and ensuring a consistent user experience.
- Build and maintain system personas and error-handling routines to handle unexpected user inputs and maintain a smooth conversation flow.
- Fine-tune LLMs, ASR models, and embedding systems under supervision, optimizing their performance and accuracy.
- Assist in running experiments using various methods such as LoRA, distillation, or pruning to enhance model efficiency and effectiveness.
- Contribute to agent evaluation metrics, regression tracking, and A/B tests, ensuring the continuous improvement of our conversational AI systems.
- Work closely with MLEs on model integration and performance tuning, ensuring a seamless collaboration between different teams and expertise.
- Partner with QA and PMs to improve agent usability, reliability, and task success rates, ensuring a positive user experience and efficient task completion.
- Help manage RAG components, context retrieval chains, and structured data inputs, ensuring the system can access and utilize relevant information effectively.
Requirements
- 4-8 years of experience in AI Engineering, Data Science, or ML-related roles, with a strong background in conversational AI and NLP.
- Proficiency in Python, SQL, and AI frameworks such as LangChain, HuggingFace, and OpenAI APIs, ensuring a solid foundation for developing AI solutions.
- Hands-on experience in LLM/NLP fine-tuning, including SFT, LoRA, QLoRA, and PEFT frameworks, to optimize model performance and accuracy.
- Strong experience with RAG development, ensuring the system can effectively retrieve and utilize relevant information for conversation.
- AWS proficiency, including S3, Lambda, API Gateway, ECS/EKS, and SageMaker, to deploy and manage AI models and applications.
- Ability to convert models into production-ready applications, including API creation, microservices, Dockers, CI/CD pipelines, and Kubernetes.
- Experience in building data/ML pipelines for transcripts, call logs, and conversation data, ensuring efficient data processing and analysis.
- Comfortable working with US engineering teams, facilitating effective cross-timezone collaboration and ensuring smooth project execution.
- Exposure to ASR/TTS outputs and voice-to-text workflows, providing an understanding of voice-based interactions and their unique challenges.
- Understanding of Conversational AI KPIs such as containment, handoff/fallback, and AHT impact, ensuring the system meets key performance indicators.
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