Xenoss
Опис
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, etc.
We work with Activision Blizzard, Toshiba, Verve Group, Microsoft, Voodoo Games,
Telefonica, and other enterprises.
We’re included in the top 100 software companies on the Inc. 5000 list.
WHAT IS THE PROJECT
The project focuses on developing a multi-agent, customizable solution that
enables financial institutions, including banks, hedge funds, and fintech
startups, to rapidly and accurately assess a company’s solvency and financial
health.
Built on a modular AI architecture, the system integrates AI agents specialised
in real-time data ingestion, financial statement analysis, risk modelling,
credit scoring, and market signal interpretation. These agents collaboratively
analyse structured and unstructured data, from filings, news, market movements,
and proprietary datasets, to deliver a comprehensive financial health profile
within minutes.
The solution is fully customizable to fit different regulatory environments,
scoring models, and industry contexts, enabling faster credit decisions,
investment risk assessments, and compliance checks at scale.
WHAT WILL YOU DO
* Take a part in designing and implementing intelligent agents as part
of a multi-agent orchestration layer. The platform is a fintech solution used
by lenders for decision-making and automation at scale
* Build and maintain backend components in Java to support LLM-driven workflows
and dynamic decision logic
* Work at the intersection of agents, LLMs, APIs, and internal data sources
* Integrate external models (e.g., OpenAI, Gemini) and services into modular,
reusable agent workflows
* Collaborate with product and data teams to deliver intelligent automation
across the platform
WHAT SHOULD YOU BRING
* 5+ years of experience as a backend or full-stack engineer(React FE), with
deep expertise in Java
* Hands-on experience designing and implementing multi-agent systems
in real-world environments
* Strong architectural thinking and ability to lead technical decisions
end-to-end
* Experience working with APIs, modular service design, and asynchronous
workflows
* Familiarity with LLMs and how they integrate into production systems (via
API or orchestration)
* Bonus: exposure to LangGraph, CrewAI, LangChain, or other agent orchestration
frameworks
WHAT WILL YOU GET
* The opportunity to own architecture and logic for agent-based systems
in a modern fintech environment
* Work on a platform used by real-world lenders to automate complex workflows
* Collaborate with an experienced team at the edge of backend engineering
and AI systems
* Remote-friendly setup with flexibility and autonomy
* No need to build ML models, focus on smart engineering orchestration around
them
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, etc.
We work with Activision Blizzard, Toshiba, Verve Group, Microsoft, Voodoo Games,
Telefonica, and other enterprises.
We’re included in the top 100 software companies on the Inc. 5000 list.
WHAT IS THE PROJECT
The project focuses on developing a multi-agent, customizable solution that
enables financial institutions, including banks, hedge funds, and fintech
startups, to rapidly and accurately assess a company’s solvency and financial
health.
Built on a modular AI architecture, the system integrates AI agents specialised
in real-time data ingestion, financial statement analysis, risk modelling,
credit scoring, and market signal interpretation. These agents collaboratively
analyse structured and unstructured data, from filings, news, market movements,
and proprietary datasets, to deliver a comprehensive financial health profile
within minutes.
The solution is fully customizable to fit different regulatory environments,
scoring models, and industry contexts, enabling faster credit decisions,
investment risk assessments, and compliance checks at scale.
WHAT WILL YOU DO
* Take a part in designing and implementing intelligent agents as part
of a multi-agent orchestration layer. The platform is a fintech solution used
by lenders for decision-making and automation at scale
* Build and maintain backend components in Java to support LLM-driven workflows
and dynamic decision logic
* Work at the intersection of agents, LLMs, APIs, and internal data sources
* Integrate external models (e.g., OpenAI, Gemini) and services into modular,
reusable agent workflows
* Collaborate with product and data teams to deliver intelligent automation
across the platform
WHAT SHOULD YOU BRING
* 5+ years of experience as a backend or full-stack engineer(React FE), with
deep expertise in Java
* Hands-on experience designing and implementing multi-agent systems
in real-world environments
* Strong architectural thinking and ability to lead technical decisions
end-to-end
* Experience working with APIs, modular service design, and asynchronous
workflows
* Familiarity with LLMs and how they integrate into production systems (via
API or orchestration)
* Bonus: exposure to LangGraph, CrewAI, LangChain, or other agent orchestration
frameworks
WHAT WILL YOU GET
* The opportunity to own architecture and logic for agent-based systems
in a modern fintech environment
* Work on a platform used by real-world lenders to automate complex workflows
* Collaborate with an experienced team at the edge of backend engineering
and AI systems
* Remote-friendly setup with flexibility and autonomy
* No need to build ML models, focus on smart engineering orchestration around
them