Who we are
Xenoss is an AI engineering and integration services company, helping medium to large enterprises run AI transformation end-to-end, from situation analysis and goals framing to data discovery and preparation, pipeline building, model development, retraining pipeline design, solution deployment, and support.
We build a broad spectrum of AI solutions such as user behaviour prediction, content generation, NLP, audience segmentation, pathfinding solutions, AI assistants, edge computer vision, fraud detection, and others.
We work with prominent companies such as Microsoft, Toshiba, AstraZeneca, Activision Blizzard, Verve Group, Voodoo Games, and Telefonica, among others.
We’re included in the top 100 software companies on the Inc. 5000 list.
What is the project
We’re hiring a Staff AI Engineer / AI Solution Architect to lead the AI architecture of a long-term In-Call Assistant initiative for a world-leading financial services company.
The project focuses on building a real-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 to live production use
What will you do
You’ll lead the applied AI architecture across the In-Call Assistant lifecycle, from data and taxonomy design to model 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
Technology landscape
You’ll operate across the modern applied AI 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
Scope of ownership and delivery context
At Staff/Architect level, you’ll own the applied AI architecture and evaluation strategy for a complex enterprise AI program.
Core ownership
Define the AI 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 use case
Act as an escalation point for AI architecture, evaluation, and data strategy decisions
Team and delivery context
Work within a cross-functional team spanning AI engineering, data engineering,
