AI Engineer​/AI Architect, Conversation Intelligence Systems; On-Site

Staff AI Engineer/AI Architect, Conversation Intelligence Systems (On-Site, New York) Who weare

Xenoss isanAI engineering and integration services company, helping medium tolarge enterprises runAI transformation end-to-end, from situation analysis and goals framing todata discovery and preparation, pipeline building, model development, retraining pipeline design, solution deployment, and support.

Webuild abroad spectrum ofAI solutions such asuser behaviour prediction, content generation, NLP, audience segmentation, pathfinding solutions, AI assistants, edge computer vision, fraud detection, and others.

Wework with prominent companies such as Microsoft, Toshiba, AstraZeneca, Activision Blizzard, Verve Group, Voodoo Games, and Telefonica, among others.

We’re included inthe top 100 software companies onthe Inc.
5000list.

We’re hiring aStaff AI Engineer/ AI Solution Architect tolead theAI architecture ofalong-term In-Call Assistant initiative for aworld-leading financial services company.

The project focuses on building areal-time conversational AI system that supports front-office employees during live customer conversations. The system identifies customer needs, objections, buying signals, and required process steps, and provides concise, context-aware recommendations.

The solution combines low-latency signal detection, context preparation, specialist recommendation generation, RAG over approved product and policy knowledge, confidence management, and compliance guardrails.

You will help define how the AI architecture, models, evaluation framework, and feedback loops are designed and evolved from the initial offline version tolive production use

You’ll lead the appliedAI architecture across the In-Call Assistant lifecycle, from data and taxonomy design tomodel training, evaluation, and production readiness.

Core work includes:

• Designing the end-to-end AI architecture for the In-Call Assistant
• Defining signal and trigger taxonomies for live conversations
• Designing training strategies for signal detection and specialist recommendation models
• Shaping data preparation, annotation, and SME validation workflows
• Evaluating fine-tuning, post-training, RAG, and hybrid approaches
• Designing low-latency signal detection, routing, context preparation, and confidence management
• Designing evaluation frameworks, golden datasets, and model improvement cycles
• Defining grounding, guardrails, abstention, and policy-compliance behavior
• Making trade-offs between model quality, latency, cost, explainability, and governance
• Partnering with AI engineers, data engineering, MLOps, and client SMEs

You’ll operate across the modern appliedAI and ML ecosystem, including:

• LLM and smaller-model training for conversational AI
• SFT, DPO/ preference optimization, LoRA/ QLoRA, and PEFT
• PyTorch and Hugging Face ecosystem
• Signal extraction and multi-label classification
• RAG and knowledge-grounded recommendation generation
• Embeddings, retrieval, and context preparation
• Low-latency model serving and inference optimization
• Golden dataset creation, annotation, and SME validation
• Model evaluation, confidence calibration, and error analysis
• MLOps, monitoring, feedback loops, and model governance

AtStaff/Architect level, you’ll own the appliedAI architecture and evaluation strategy for acomplex enterprise AIprogram.

Core ownership

• Define theAI approach for the conversation intelligence PoC
• Establish the event/ intent/ insight taxonomy
• Define the golden dataset strategy and annotation workflow
• Establish evaluation frameworks and acceptance criteria
• Drive trade-offs between accuracy, explainability, latency, cost, and governance
• Decide which modeling approaches are appropriate for each…