Senior AI Scientist: Enterprise AI Systems

Senior AI Scientist:
Enterprise AI Systems page is loaded## Senior AI Scientist:
Enterprise AI Systems locations:
Spain - Valenciatime type:
Full time posted on:
Posted Todayjob requisition :
JR100923

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 a Sr. AI Scientist to design, experiment with, and product ionize AI systems for enterprise use cases. This role focuses on building reliable AI workflows, developing strong 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 a Sr. AI Scientist, you will operate at the intersection of research and applied engineering—
** owning*
* ** scoped, end‑to‑end AI workflows
** where appropriate—while partnering closely with platform and product teams to ensure scientific rigor, reliability, and measurable business impact.##

Key Responsibilities
** AI System Design & Implementation
*** Design and implement AI systems capable of handling complex workflows, including multi step reasoning, planning, and decision making.
* Explore and apply various orchestration patterns and architectures to solve business problems effectively.
* Translate abstract requirements into reliable AI behaviors under real operational constraints such as latency, cost, and safety.
** Experimentation & Evaluation
*** Lead
** systematic experimentation
** across prompts, agents, model variants, and tool configurations.
* Define and implement
** evaluation strategies for agentic systems**, including task success, robustness, failure recovery, and hallucination analysis.
* Develop lightweight benchmarks, simulations, and offline/online evaluation loops to guide rapid iteration and decision‑making.
** Model Optimization & SLMs
*** Fine‑tune and adapt
** small and medium language models
** using techniques such as PEFT, SFT, and distillation.
* Balance performance, latency, and cost for enterprise‑grade workloads.
* Contribute to model selection decisions (SLMs vs. larger models) based on use‑case requirements and empirical evidence.
** Applied End‑to‑End Delivery
*** Own
** bounded, end‑to‑end AI workflows
** from problem framing through deployment where this accelerates impact.
* Partner with engineering teams on integration, monitoring, and lifecycle management.
* Contribute to production readiness without acting as a full‑time platform or infrastructure owner.
** Technical Leadership
*** Act as a technical reference point for
** agentic AI best practices
** across the team.
* Mentor junior scientists on experimentation methodology, evaluation design, and practical GenAI system development.
* Influence roadmap and technical direction through
** evidence‑backed recommendations**.##

Qualifications & Skills
* ** Education**: MS or PhD in Artificial Intelligence, Computer Science, or a related quantitative field.
* ** Applied ML / GenAI Experience**: 5+ years building and iterating on ML systems, with hands‑on experience using Transformer‑based models.
* ** Model Development & Deployment**:
Strong experience in one or more areas: model training, fine tuning, optimization, or production deployment of ML systems.
* ** Experimentation Mindset**:
Strong intuition for experimental design,…