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, solution deployment, and support.

We build a broad spectrum of AI solutions such as user behaviour prediction, content generation, NLP, audience segmentation, 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 Full-Stack 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 relevant conversation signals and provides concise, context-aware recommendations.

You will primarily work on the application and integration layer connecting live conversation data, AI models, enterprise data sources, backend services, and the employee-facing web interface.

The broader solution includes real-time transcription, signal detection, context preparation, recommendation generation, RAG, feedback capture, and production monitoring.

What will you do

You’ll build and integrate the application layer around the AI models, working closely with the AI Solution Architect, AI engineers, MLOps, and client engineering teams.

Core work includes:

Building backend services and APIs for the real-time In-Call Assistant
Building the employee-facing web UI for presenting recommendations and conversation context
Implementing real-time data flows from conversation input to recommendation delivery
Integrating transcription, AI inference, CRM, customer context, and internal APIs
Implementing the context-preparation and orchestration layer between system components
Implementing feedback capture for recommendation acceptance, dismissal, and user actions
Handling authentication, authorization, logging, and enterprise integration requirements
Optimizing application performance, reliability, and end-to-end latency
Supporting deployment, observability, and production troubleshooting

The proposal explicitly includes a context-preparation harness, real-time inference pipeline, UI delivery, feedback capture, and deployment/handover as core solution components.

Technology landscape

You’ll operate across a modern cloud and AI application stack, including:

Python and/or TypeScript
React or similar modern frontend frameworks
FastAPI, https://jobeax.com/link/JSd7UYCSg572jJDY, or equivalent backend frameworks
REST and streaming APIs
WebSockets, Server-Sent Events, or similar real-time communication patterns
Event-driven and asynchronous processing
Integration with AI/ML inference services
Enterprise API and data integrations
Authentication and authorization
Logging, tracing, and observability
Containerized deployment and CI/CD
Cloud infrastructure and managed services

We optimize for reliability, low latency, maintainability, and enterprise constraints.

Scope of ownership and delivery context

Core ownership

Build the application layer connecting AI models with enterprise systems
Build and evolve the employee-facing web UI
Implement real-time APIs and orchestration flows
Integrate customer, CRM, product, and policy context into the AI pipeline
Implement recommendation delivery and feedback capture
Ensure application-level reliability, observability, and performance
Support transition from offline prototype to live pilot and production

Team and delivery context

Work closely with the AI Solution Architect and two AI/ML engineers
Collaborate with client backend, infrastructure, security, and integration teams
Integrate with systems and APIs managed by the client
Work inside client-controlled infrastructure
Contribute to architecture and integration decisions through hands-on implementation
Work with part-time UI/UX support while owning the frontend implementation

What should you bring

Must have:

Strong hands-on experience building production web applications and backend services
Strong experience with Python and/or TypeScript
Strong experience with modern frontend development, preferably React
Experience designing and integrating REST APIs and asynchronous services
Experience with real-time or event-driven systems
Experience integrating external services and enterprise APIs
Experience working with AI/ML inference APIs or AI-enabled applications
Strong understanding of backend reliability, error handling, and observability
Experience with authentication and authorization
Understanding of application performance and latency optimization
Experience with cloud environments and containerized applications
Ability to work effectively across backend, frontend, infrastructure, and AI teams

Nice to have:

Experience building real-time AI assistants or copilots
Experience with WebSockets, SSE, Kafka, Pub/Sub, or similar technologies
Experience integrating speech / transcription systems
Experience with LLM or ML serving infrastructure
Experience with CRM or enterprise workflow integrations
Financial services domain exposure
Experience with regulated or security-sensitive environments
Kubernetes and production observability experience
Experience building internal enterprise tools or agent-assist interfaces

Operating model

Engagement structure: Full-time, long-term B2B contract
Work location: Remote, EU-based
Time-zone overlap: At least 4 working hours overlapping with the New York team
Infrastructure: Client environment only
Data residency: All work executed within the client perimeter
Delivery mode: Initial offline prototype followed by controlled live pilot and production evolution

Senior Full-Stack Engineer (BE-heavy), Real-Time AI Applications (Europe)

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