ML Engineer

ROLE OVERVIEW

As an NLP Engineer, you will help design, train, evaluate, and improve language models and conversational AI systems used across Proto's products and deployments.

You will work closely with Product, CX, Engineering, and AI teams to improve multilingual understanding, intent recognition, response quality, retrieval systems, and AI agent performance across voice and chat experiences.

This role is ideal for someone who enjoys hands-on experimentation, model optimization, and turning AI research into production-ready solutions.

KEY RESPONSIBILITIES

• Develop and improve NLP pipelines for conversational AI systems
• Fine-tune and evaluate large language models (LLMs)
• Build multilingual NLP capabilities for low-resource languages
• Work on intent classification, entity extraction, semantic search, summarization, and retrieval systems
• Improve AI response quality, latency, and accuracy
• Collaborate with CX and Product teams to analyze user conversations and improve AI behavior
• Design prompt engineering and RAG workflows
• Work with speech-to-text and text-to-speech integrations when needed
• Create evaluation metrics and benchmarking processes for AI performance
• Support deployment and monitoring of NLP models in production environments
• Research new NLP approaches and recommend improvements to Proto's AI stack

QUALIFICATIONS

• Bachelor's degree in Computer Science, AI, Data Science, Linguistics, or related field
• Strong understanding of NLP concepts and machine learning fundamentals
• Experience with Python and NLP frameworks such as:
  • Hugging Face
  • spaCy
  • NLTK
  • LangChain
  • LlamaIndex
• Experience working with LLMs and prompt engineering
• Familiarity with vector databases and semantic retrieval systems
• Experience handling structured and unstructured datasets
• Understanding of APIs, integrations, and cloud environments
• Strong analytical and problem-solving skills
• Ability to work independently in a remote and fast-paced environment

• Experience with multilingual or low-resource language models
• Experience in voice AI or conversational AI platforms
• Knowledge of speech technologies (ASR/TTS)
• Familiarity with RAG architecture and AI agent workflows
• Experience deploying models using Docker or Kubernetes
• Exposure to healthcare, fintech, or government AI systems