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