Data Scientist

Yum! Brands’ is hiring AI Engineers to help design and improve voice-based AI agents for Taco Bell drive-thru operations. These roles are perfect for early-career AI engineers or data scientists looking to expand their skills in LLM-based interaction design, speech system optimization, and production-quality prompt development.

You’ll collaborate closely with senior AI Engineers, MLEs, and QA to iterate on agent prompts, tune foundational models, and contribute to the overall agent experience for customers and employees.

Responsibilities

Prompt Engineering & Agent Design

Author and refine prompt instructions, chaining logic, and fallback strategies

Design and test multi-turn conversation flows aligned to Taco Bell brand voice

Build and maintain system personas and error handling routines

Model Tuning & Evaluation

Fine-tune LLMs, ASR models, and embedding systems under supervision

Assist in running experiments using LoRA, distillation, or pruning methods

Contribute to agent evaluation metrics, regression tracking, and A / B tests

Cross-Functional Collaboration

Work closely with MLEs on model integration and performance tuning

Partner with QA and PMs to improve agent usability, reliability, and task success rates

Help manage RAG components, context retrieval chains, and structured data inputs

Mandatory Skills

4-8 years of experience in AI Engineering, Data Science or ML-related roles

Proficiency in Python, SQL and AI frameworks (e.g., LangChain, HuggingFace, OpenAI APIs)

Hands-on experience in LLM / NLP fine-tuning, SFT, LoRA, QLoRA, PEFT frameworks

Strong experience with RAG development

AWS proficiency (S3, Lambda, API Gateway, ECS / EKS, possibly SageMaker)

Ability to convert models into production-ready applications : - API creation, Microservices, Dockers, CI / CD pipelines, Kubernetes

Experience in building data / ML pipelines for : transcripts, call logs & conversation data.

Comfortable working with US engineering teams (cross-timezone collaboration).

Preferred / good to have :

Exposure on ASR / TTS outputs (voice-to-text workflows)

Understanding of Conversational AI KPIs (containment, handoff / fallback, AHT impact, etc.)

Any experience with real-time orchestration (e.g., routing calls, streaming pipelines)

Familiarity with audio / voice analytics

Experience in deploying LLMs in cloud environments beyond AWS (optional).