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 Senior AI/ML Engineer to join 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.
You will primarily work on the signal detection and trigger layer: turning live conversation streams into structured signals, confidence scores, and routing decisions under strict latency requirements.
The broader solution combines low-latency signal detection, context preparation, specialist recommendation generation, RAG over approved product and policy knowledge, and compliance guardrails.
What will you do
You’ll own implementation and continuous improvement of the signal detection and trigger model, working closely with the AI Solution Architect and the rest of the AI team.
Core work includes:
Turning conversation transcripts into structured signals, intents, and trigger events
Building and training multi-label classification models for live conversation analysis
Preparing training datasets using LLM-assisted annotation and SME-validated labels
Developing embeddings, classifiers, and alternative modeling approaches for signal detection
Designing and tuning confidence thresholds, routing logic, and abstention behavior
Optimizing inference for low latency and high throughput
Building evaluation datasets, metrics, and error-analysis workflows
Running experiments and model comparisons across accuracy, latency, and data requirements
Investigating false positives, false negatives, and model failure patterns
Working with AI, data, MLOps, and client teams to move models from experimentation to production
You’re expected to be deeply hands-on in model development, training, evaluation, and optimization.
Technology landscape
You’ll operate across the modern applied AI and ML ecosystem, including:
Python
PyTorch and Hugging Face
NLP and conversational AI
Embeddings and sentence encoders
Multi-label classification
Lightweight neural classifiers
Classical ML and gradient boosting where appropriate
LLM-assisted data annotation
Confidence calibration and threshold optimization
Model evaluation and error analysis
Low-latency inference and serving
MLOps, monitoring, and model lifecycle tooling
We optimize for measurable model quality, low latency, production viability, and enterprise constraints.
Scope of ownership and delivery context
Core ownership
Implement and improve the Signal Extraction & Trigger model
Build training and evaluation pipelines for signal detection
Work with the architect and client SMEs on signal taxonomy and labeling
Establish measurable model quality across precision, recall, false positives, and false negatives
Tune confidence thresholds and trigger behavior
Optimize models for real-time inference constraints
Contribute to production monitoring and continuous model improvement
The proposal specifically describes Model 1 as an always-on component processing the rolling transcript, producing
