Job description
Ideal Candidate Profile A hands-on practitioner who has successfully deployed end-to-end AI solutions into production, combining machine learning, cloud engineering, data pipelines, and AI agents. Examples may include AI-powered fraud detection, mule account identification, suspicious transaction monitoring, investigation automation, or intelligent case management solutions.
Key Responsibilities
Risk Analytics & Machine Learning
Design, build, and deploy machine learning models for fraud detection, anomaly detection, risk scoring, and financial crime prevention.
Develop predictive analytics solutions supporting risk and compliance decision-making.
Monitor model performance and continuously improve production outcomes.
Work closely with Risk, Compliance, and Business stakeholders to define measurable business objectives and success criteria.
AI Agents & Intelligent Automation
Design and deploy AI agents to automate risk investigation and operational workflows.
Leverage Generative AI and LLM technologies to improve productivity and decision support.
Build solutions for
Mule account detection
Suspicious Transaction Report (STR) preparation
Financial crime investigations
Alert triage and case summarization
Risk monitoring and operational automation
Data Engineering & Platform Development
Design and maintain ETL/ELT pipelines supporting analytics and AI workloads.
Develop DAG-based workflow orchestration and automation.
Ensure high-quality, reliable, and scalable data processing capabilities.
Cloud & AI Platform Engineering
Deploy and manage AI/ML workloads on cloud-native platforms.
Implement MLOps practices, model monitoring, and deployment automation.
Contribute to scalable, secure, and resilient AI platforms.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related disciplines.
5+ years of experience in Data Science, Machine Learning,
AI Engineering, or Advanced Analytics.
Proven experience building and deploying machine learning models into production environments.
Strong Python programming skills and hands-on experience with modern ML frameworks.
Experience designing and maintaining ETL/ELT pipelines.
Experience with workflow orchestration tools and DAG-based automation (e.g., Airflow or equivalent).
Hands-on cloud experience (AWS preferred).
Experience deploying and operating AI/ML solutions on cloud-native platforms.
Practical experience with Generative AI, LLMs, AI Agents, and agentic workflows.
Ability to explain technical solutions in terms of business impact, risk reduction, and operational outcomes.
Preferred Qualifications
Banking, fintech, or financial services experience.
Experience in fraud analytics, AML, transaction monitoring, mule account detection, or financial crime prevention.
Experience deploying production-grade AI agents.
Experience supporting regulatory and compliance-related initiatives.
(Note: Due to the high volume of applications we receive, we are unable to respond to every candidate individually. If you have not received a response from GFT regarding your application within 10 workdays, please consider that we have decided to proceed with other candidates. We truly appreciate your interest in GFT and thank you for your understanding)