AI Scientist: Enterprise AI Systems

Uniphore is one of the largest B2B AI-native companies—decades-proven, built-for-scale and designed for the enterprise. The company drives business outcomes, across multiple industry verticals, and enables the largest global deployments.  
  
Uniphore infuses AI into every part of the enterprise that impacts the customer. We deliver the only multimodal architecture centered on customers that combines Generative AI, Knowledge AI, Emotion AI, workflow automation and a co-pilot to guide you. We understand better than anyone how to capture voice, video and text and how to analyze all types of data.  
  
As AI becomes more powerful, every part of the enterprise that impacts the customer will be disrupted. We believe the future will run on the connective tissue between people, machines and data: all in the service of creating the most human processes and experiences for customers and employees.   

Job Description:
 

Job Description Summary

We are seeking an AI Scientist to design, experiment with, and help productionize AI systems for enterprise use cases. This role focuses on building reliable AI workflows, contributing to experimentation and evaluation frameworks, and optimizing language models (including small and mid-sized models) for real-world deployment. Experience with agentic systems is a plus.

About the Role

As an AI Scientist, you will work at the intersection of research and applied engineering—contributing to scoped AI workflows while partnering closely with platform and product teams to ensure scientific rigor, reliability, and measurable business impact. You will play a hands-on role in experimentation, model adaptation, and iterative system improvement.

Key Responsibilities

AI System Design & Implementation

• Contribute to the design and implementation of AI systems capable of handling structured workflows, including multi-step reasoning and decision-making.
• Apply orchestration patterns and architectures under guidance to solve business problems effectively.
• Translate product requirements into reliable AI behaviors while considering operational constraints such as latency, cost, and safety.

Experimentation & Evaluation

• Design and run systematic experiments across prompts, model variants, tool configurations, and agent setups.
• Implement evaluation strategies for AI systems, including task success, robustness, and failure analysis.
• Build lightweight benchmarks, simulations, and offline/online evaluation loops to support rapid iteration.
• Perform detailed error analysis to guide model and workflow improvements.

Model Optimization & SLMs

• Fine-tune and adapt small and medium language models using techniques such as PEFT, SFT, and distillation.
• Assist in balancing performance, latency, and cost for enterprise-grade workloads.
• Support model selection decisions (SLMs vs. larger models) based on experimentation and performance data.

Applied Delivery & Collaboration

• Contribute to defined AI workflows from problem framing through deployment.
• Partner with engineering teams on integration, monitoring, and lifecycle management.
• Support production readiness through testing, evaluation, and performance optimization.

Technical Growth & Collaboration

• Share learnings and best practices related to GenAI experimentation and system development.
• Collaborate with senior scientists and engineers to refine methodologies and technical direction.
• Contribute evidence-backed insights to roadmap discussions.

Qualifications & Skills

• Education: MS or PhD in Artificial Intelligence, Computer Science, or a related quantitative field.
• Applied ML / GenAI Experience: 2–4+ years building and iterating on ML systems, with hands-on experience using Transformer-based models.
• Model Development & Deployment: Experience in one or more areas: model fine-tuning, optimization, experimentation, or deployment of ML systems.
• Experimentation Mindset: Strong foundation in experimental design, evaluation metrics, error analysis, and iterative improvement.
• Model Adaptation: Familiarity with techniques such as fine-tuning, PEFT, SFT, distillation, or related optimization approaches.
• Programming: Strong Python skills and solid software engineering fundamentals.
• Production Awareness: Exposure to deploying AI systems in production environments (cloud, CI/CD, monitoring), with a desire to deepen expertise.
• Bonus: Experience building agentic workflows, tool-using systems, or autonomous AI agents (e.g., LangGraph, custom agent frameworks); RLHF experience is a plus.

Location preference:

Uniphore is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.
 
For more information on how Uniphore uses AI to unify—and humanize—every enterprise experience, please visit www.uniphore.com.