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