Middle AI Engineer / Data Scientist

Join the global leader in cloud contact center technology, where innovation meets real-world impact. Our client empowers enterprises worldwide to deliver seamless, personalized, and delightful customer experiences.

About the Role:

We are building a new AI Deployment Engineering Team that will serve as a key link between Professional Services and Product Engineering. The team will develop customer-facing AI solutions, customize product features based on real needs, and create reusable templates that can be deployed across multiple clients.

As a Middle AI Engineer / Data Scientist, you will gain hands-on experience with NLP, machine learning, and AI systems while working closely with experienced engineers, conversation designers, and consultants. This is a unique opportunity to join a team from the ground up and grow your technical and client-facing skills.

Responsibilities:

• Build and improve AI chatbots and virtual agents using real conversational data.
• Clean, prepare, and analyze text data for NLP and ML models.
• Evaluate model performance and propose improvements.
• Work directly with customers to gather requirements and communicate progress.
• Collaborate with engineers, consultants, and conversation designers.
• Develop reusable solutions and provide feedback to Product Engineering.

• 4+ years of experience with Python and ML/NLP libraries (e. g. , Pandas, Scikit-learn, PyTorch, TensorFlow).
• Hands-on experience with NLP tasks: text classification, entity extraction, chatbots, or conversational AI.
• Solid understanding of ML fundamentals, statistics, and model evaluation metrics.
• Experience working with AI/LLM applications or integrating ML into production systems.
• Strong communication skills; comfortable collaborating with customers and cross-functional teams.
• Ability to work independently, handle ambiguity, and manage priorities.
• Bachelor s degree in Computer Science, Data Science, Linguistics, or related field.

Nice to have:

• Customer-facing experience, cloud platforms (AWS/GCP/Azure), or CI/CD for ML models.