Who we are

Xenoss is an innovative AI Lab and software engineering company delivering advanced solutions to enterprise clients in the AdTech sector.

We build high-load advertising and data platforms and solve applied AI challenges across programmatic advertising, audience intelligence, campaign optimization, anomaly detection, NLP, LLMs, AI agents, and data engineering.

We work with Activision Blizzard, Toshiba, Verve Group, Voodoo Games, Telefónica, and other enterprises. Xenoss is included among the top 100 software companies on the Inc. 5000 list.

What is the project

Client: A leading global AdTech company operating an AI-powered advertising platform that connects major publishers, advertisers, agencies, data providers, and commerce media networks across CTV, mobile, and omnichannel environments.

Solution: Development of an agentic advertising platform that enables AI agents to plan, execute, and optimise programmatic campaigns from natural-language instructions. The project covers

extending MCP-based campaign management tools,
building an automated MCP testing framework,
developing a Campaign QA agent for detecting configuration issues, anomalies, and potential delivery risks.

Engagement model: You will join the Xenoss team that works directly with the client’s Agentic AI product team and work directly with engineers, product managers, and business stakeholders. The role involves contributing to production-grade agent workflows, MCP tools, backend services, integrations, automated evaluation, and observability within the client’s existing GenAI platform and engineering framework.

What will you do

Own assigned components from technical design through production deployment.
Extend MCP-based tools for campaign management, reporting, targeting, and advertiser operations.
Build backend services, agent workflows, integrations, validation logic, and observability.
Develop an automated testing framework for MCP clients, hosts, and tools, including deterministic and LLM-based evaluation.
Build a Campaign QA agent to detect configuration issues, anomalies, and potential delivery risks.
Collaborate with engineering, product, and business stakeholders, while maintaining strong testing, documentation, and production-readiness standards.

Technology landscape

5+ years with Python and FastAPI
LLM-powered applications and agent workflows
LangGraph and LangChain
MCP tools, clients, hosts, and agent-to-agent communication
CRM, messaging, analytics, and internal enterprise platforms
Docker and Kubernetes
Git and CI/CD pipelines
Automated agent testing, tool-call tracing, JSON Schema validation, and LLM-as-a-Judge evaluation
Automated testing, observability, monitoring, and alerting

What should you bring

Strong commercial experience in Python backend engineering and building production-grade AI applications or agentic systems.
Hands-on experience designing APIs, integrations, tool-based workflows, automated tests, and production observability.
Experience evaluating non-deterministic AI systems and troubleshooting them in production.
Understanding of programmatic advertising and core campaign concepts, such as line items, creatives, targeting, budgets, flighting, and delivery.
Ability to independently own components from technical design through deployment.
Strong English communication skills and experience working with engineering, product, and business stakeholders

Nice to have

Previous experience building AdTech, DSP, SSP, campaign management, ad serving, or advertising analytics products.
Hands-on experience with MCP clients, hosts, tools, or protocol-compliant integrations.
Experience building automated evaluation frameworks for LLM or agentic systems.
Familiarity with LLM-as-a-Judge, tracing, schema validation, and ground-truth-based testing.
Experience with anomaly detection, rule-based validation, or campaign quality assurance.
Understanding of Native advertising, creative flighting, audience targeting, and campaign reporting.