Uniphore is building the world's best-in-class Business AI platform to enable business users to leverage the advances of artificial intelligence to solve problems in their specific business processes. Uniphore provides a composable sovereign and secure solution to business users and drives AI adoption in organizations with innovative solutions to building serving and optimizing these AI solutions. 

Location: Palo Alto CA 

You will be working at the core of a category-defining product — building the agent learning platform and SLM AI flywheel that powers our Business AI cloud. This is the system that closes the loop: AI agents operate in production capture real-world signal and continuously improve through automated fine-tuning and evaluation. If you are ready to own the full ML stack behind agentic AI at enterprise scale — from orchestration architecture through model optimization evaluation infrastructure and production delivery — come join us. 

Responsibilities 

Agentic AI Architecture & Orchestration 

• Design and implement production-grade agentic systems capable of multi-step reasoning planning tool use and decision-making under real operational constraints (latency cost safety). 

• Own the orchestration layer of the agent learning platform: agent memory inter-agent communication failure recovery and reliability patterns at enterprise scale. 

• Translate abstract product requirements into reliable AI behaviors and set the architectural standards the team builds against. 

Agent Learning & SLM Optimization 

• Own the closed-loop learning pipeline: capturing production signal from deployed agents triggering fine-tuning cycles and gating model promotion into production. 

• Fine-tune and adapt small and medium-sized foundation models using techniques such as PEFT SFT distillation and reinforcement learning (RLHF DPO). 

• Drive model selection decisions (SLMs vs. larger models) based on use-case requirements latency SLAs and empirical evidence. 

Evaluation & Experimentation 

• Define and build evaluation strategy for agentic systems: task success metrics trajectory evaluation hallucination analysis and regression detection across the learning flywheel. 

• Develop offline and online evaluation loops — including LLM-as-judge frameworks — that guide rapid iteration and provide the ground truth signal the flywheel depends on. 

• Lead systematic experimentation across prompts agent configurations model variants and tool integrations. 

End-to-End Delivery & Production Ownership 

• Own bounded end-to-end ML workflows from problem framing through deployment monitoring and lifecycle management. 

• Partner with engineering on integration observability and production readiness — without acting as a full-time infrastructure owner. 

• Identify systemic gaps across the ML stack (accuracy latency cost reliability) and lead the work to close them. 

Technical Leadership 

• Act as the technical reference point for agentic AI and SLM best practices across the team. 

• Drive cross-functional alignment with product and engineering through evidence-backed technical recommendations that influence the roadmap. 

• Mentor senior and mid-level engineers on experimentation methodology evaluation design and production ML system development. 

We're Eager to Work With 

• Those who want to join our mission to democratize AI to more business users. 

• Individuals with an unwavering customer focus committed to crafting experiences that delight. 

• Those who embrace excellence and consistently deliver top-tier work. 

• Innovators who thrive on creative thinking and daring to tread new paths. 

• Owners at heart ready to take responsibility and drive results. Individuals of the highest integrity who foster trust and honesty. 

Minimum Qualifications 

• Education: MS or PhD in Computer Science Machine Learning Statistics or related field 

• Experience: 8+ years designing building and operating production ML systems with hands-on experience with frontier and open-source models 

• Proven track record owning agentic AI systems or closed-loop model improvement pipelines in production — not prototype quality 

• Deep experience with LLM or SLM fine-tuning: SFT RLHF/DPO data curation and rigorous evaluation design 

• Experience translating business impact into quantitative metrics and designing statistically sound experiments 

• Track record of influencing technical decisions beyond your immediate team — through design docs architectural reviews or cross-functional alignment 

• Strong communication skills verbal and written with ability to present technical strategy to non-technical stakeholders 

Preferred Qualifications 

• Experience designing evaluation frameworks for agentic systems (trajectory eval task success robustness benchmarks 

• Demonstrated influence at org or platform level: architectural standards or platform decisions that multiple teams built against 

• Familiarity with agentic orchestration frameworks ( LangGraph or equivalent) 

• Background in enterprise NLP conversational AI or contact center / CX domains 

• Publications at top-tier peer-reviewed venues or significant open-source contributions in relevant areas 

• Experience at fast-growing companies or in agile high-ownership engineering environments 

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Uniphore and has not been reviewed or approved by Uniphore.