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AI Summary
About the Role
As an AI Engineer, you’ll develop algorithms that improve key business areas, including:
User and merchant experience
Transactional intelligence
Personalization and discovery
Risk, fraud, and behavioral understanding
You’ll solve complex data challenges in payments and financial services, building intelligent systems that balance growth, personalization, and risk management.
You’ll own the full lifecycle of machine learning and LLM services, from design to deployment and optimization, working closely with product, engineering, and business teams to deliver scalable data science solutions.
Key Responsibilities:
Model Development and Deployment: Build and deploy ML models for personalization, ranking, user behavior modeling, and financial decisioning.
LLM Service Development: Design and deploy multi-agentic LLM services for internal and external use.
Data Expertise: Work with large-scale structured and unstructured transactional, behavioral, and textual data.
Methodology Application: Use classical ML and representation learning (embeddings, similarity models, sequence-based models).
ML-Ops and Scalability: Apply ML-Ops best practices to ensure reliable, scalable, production-ready models.
Experimentation and Impact: Run A/B tests, evaluate results statistically, and translate insights into product decisions.
Cross-functional Collaboration: Partner with product, engineering, risk, and analytics to implement data-driven solutions.
Communication: Communicate insights, model behavior, and trade-offs to technical and non-technical stakeholders.
Requirements and Experience
Bachelor’s degree in Computer Science/Engineering or equivalent practical experience.
Strong software engineering fundamentals.
Proven applied Data Science experience across ML models and techniques.
Solid ML knowledge (classification, regression, clustering, ranking, time-series).
Proficiency in Python and ML libraries (e.g., scikit-learn, PyTorch, TensorFlow).
Strong SQL skills for large-scale data.
Payments/fintech/banking experience is advantageous.
Experience with transactional data, user behavior, or risk management is a plus.
Strong analytical skills to turn business problems into data science solutions.
Technical and Methodological Skills
Experience with transformers, embeddings, or representation learning.
Familiarity with CI/CD and version control (Git).
Experience with Agile practices (Scrum, Kanban, Lean).
Communication and Level
Excellent written and verbal communication skills in English.
Open to Mid-Level and Specialist/Senior Specialist experience tiers.
Tech Stack
Languages: Python (primary), SQL (advanced), Java (nice to have)
Streaming & Messaging: Apache Kafka
Workflow Orchestration: Apache Airflow
DevOps & Infrastructure: Docker, Kubernetes, GitLab CI, Terraform
Version Control: Git
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