Staff AI Architect: Real-Time In-Call Assistant

Xenoss; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Xenoss is seeking a Staff AI Engineer / AI Solution Architect to lead the applied AI architecture for a long-term In-Call Assistant initiative in New York, NY (onsite). This role shapes a real-time conversational AI system with compliance guardrails and confidence management, guiding PoC-first development toward production-grade conversation intelligence.

You will define the end-to-end AI approach, set taxonomies for live conversations, and collaborate with AI engineering, data engineering,

Qualifications

• Hands-on experience with applied AI/ML in production environments.
• Experience with NLP or transcript-based systems.
• Ability to design evaluation frameworks, not only run experiments.
• Experience building or validating structured datasets from unstructured text.
• Strong knowledge of LLM-based extraction, RAG, and fine-tuning trade-offs.

Responsibilities

• Lead the applied AI architecture across the In-Call Assistant lifecycle, from data design to production readiness.
• Design end-to-end AI architecture for the In-Call Assistant.
• Define signal/taxonomies for live conversations.
• Design training strategies for signal detection and specialist recommendation models.
• Shape data preparation, annotation, and SME validation workflows.
• Evaluate fine-tuning, post-training, RAG, and hybrid approaches.
• Design low-latency signal detection, routing, and confidence management.
• Define grounding, guardrails, policy-compliance behavior, and governance.

Skills

Applied AI/ML

NLP/Conversational AI

LLM Extraction

RAG & Fine-tuning

MLOps

Data Wrangling

Evaluation Frameworks

Tools

PyTorch

Hugging Face

RAG Pipelines

Job description

Xenoss is seeking a Staff AI Engineer / AI Solution Architect to lead the applied AI architecture for a long-term In-Call Assistant initiative in New York, NY (onsite). This role shapes a real-time conversational AI system with compliance guardrails and confidence management, guiding PoC-first development toward production-grade conversation intelligence.

You will define the end-to-end AI approach, set taxonomies for live conversations, and collaborate with AI engineering, data engineering,