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).