Senior AI Engineer

EXUS  is a global technology company specializing in debt collections software for financial services and utilities. Our enterprise SaaS platform is used in over 50 countries worldwide, delivering measurable improvements in collections, compliance, and operational efficiency. With 20+ years of experience and a product recognized by Gartner as best-in-class, we combine global insight with local adaptability to empower collections teams worldwide. 

Our people constitute the source of inspiration that drives us forward and help us fulfill our purpose of being role models for a better world. 

This is your chance to be part of a highly motivated, diverse, and multidisciplinary team, which embraces breakthrough thinking and technology to create software that serves people. We offer a creative, fun, and above all, inspiring working environment that fosters team spirit and promotes the greater good. We are positive and eager to learn and explore. We are committed to our vision. 

Our shared Values: 

• We are transparent and direct 

• We are positive and fun, never cynical or sarcastic 

• We are eager to learn and explore 

• We put the greater good first 

• We are frugal and we do not waste resources 

• We are fanatically disciplined, and we deliver on our promises 

We are EXUS! Are you? 

EXUS is looking for a talented Senior AI Engineer to join us in building the next generation of intelligent credit risk and collections systems. This is a remote-first role, with the opportunity to collaborate in hybrid mode at our Athens offices alongside cross-functional teams shaping AI-powered features for real-world impact. 
 

As a Senior AI Engineer, you will be designing, deploying, and operating production-grade ML/GenAI systems that power credit-risk scoring, early-warning, and digital collections optimization inside our EXUS Financial Suite (EFS). You will work end-to-end: from data understanding and feature engineering on transactional, behavioral, and bureau data, through model training and evaluation, to robust deployment, monitoring (including data and concept drift), and continuous improvement. You will also help design LLM-powered workflows and agents that complement core risk models and improve collections productivity, always with a focus on reliability, explainability, and business value. 

• BSc in Computer Science, Engineering, Mathematics, Physics, or related STEM field (MSc/PhD a plus) 
• At least 5 years of experience designing, building , and running ML/AI solutions in production (end-to-end from data to deployment), including hands-on work with LLMs or Generative AI 
• Experience in financial services, credit risk, or banking is a strong plus (e.g., credit scoring, early-warning models, collections segmentation/strategy, propensity-to-pay, limit management) 
• Skilled in Python, writing clean, modular, and tested code with async handling, dependency management, and testing practices; familiarity with GenAI coding assistants (e.g., Cursor, Copilot) is a plus 
• Proficient in ML/DL frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow for supervised, unsupervised and time-series modeling, and experienced in turning these models into robust, scalable services 
• Strong experience with MLOps/LLMOps and observability tools (e.g., MLflow, Kubeflow, LangSmith, Langfuse) for experiment tracking, model lifecycle management, CI/CD of models, and end-to-end monitoring in production 
• Solid hands-on experience with data and feature engineering  
• Experience in monitoring and improving production models, including data and concept drift detection, model explainability, and interpretability techniques (e.g., feature importance, SHAP/ICE, scorecards) and the ability to communicate model behaviour to risk, business, and compliance stakeholders.
• Hands-on experience building reliable ML/LLM workflows (e.g., scoring services, RAG pipelines, AI agents) using frameworks such as LangGraph, LangChain, Pydantic AI or similar, with attention to testing, observability, and performance in production 
• Familiar with modern model training and fine-tuning approaches (e.g., classical supervised learning pipelines, Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF)) and how to take trained models safely into production 
• Solid theoretical foundation in statistics, probability, optimization, and ML fundamentals such as bias/variance trade-offs, loss functions, and evaluation metrics 

Nice-to-Have Skills:

• Experience with CI/CD and observability using Git-based automation (e.g., GitHub Actions, GitLab CI) and monitoring tools like Prometheus, Grafana, or OpenTelemetry 
• Exposure to data and feature pipelines with tools such as Airflow, Spark, or Kafka, including designing or contributing to workflows and feature stores 
• Familiarity with cloud-native development on AWS, Azure, or GCP, including containerized deployment with Docker/Kubernetes and basic infrastructure-as-code 
• Understanding of API and system architecture, including event-driven design and API exposure (REST/gRPC), with the ability to reason about latency, throughput, and scaling trade-offs 
• Awareness of security and compliance in ML, including responsible-AI practices, model governance, and secure deployment (e.g., GDPR, MLOps access control)

General Skills:

• Curious and inventive spirit; motivated to explore emerging techniques, experiment boldly, and translate ideas into working systems.  
• Thrive in fast-moving, autonomous squads; action-oriented with a focus on continuous improvement.  
• Excellent command of the English language (both verbal and written); clear communicator able to convey complex models into business value for non-technical stakeholders.  
• Strong problem-solving and analytical thinking skills.  
• Team player, self-motivated, and constantly seeking new knowledge.  
• Growth mindset with strong alignment to EXUS values.  
• Fulfilled military obligations (If applicable)

At EXUS we help our people to achieve excellent results by creating a work environment that encourages individual and team success. 

• Fully remote work setup 

• Competitive salary 

• Inclusive work environment & Well-being Program 

• A clear induction program & a mentoring buddy to help you 

• Private health insurance allowance  

• Unlimited time off