Machine Learning Engineer (Financial AI, LLM Training)

About Us

GönderAL is a financial technology company focused on building secure and scalable digital payment platforms. We design systems that support real-time transactions, robust infrastructure, and user-friendly interfaces tailored for modern financial operations. We operate in a dynamic, collaborative environment focused on secure, reliable, and customer-centered financial services.

For us, impact is the driver of our work. We value ownership, practical problem-solving, and teamwork to deliver meaningful solutions for our customers and partners.

The Mission

We’re looking for a Machine Learning Engineer to develop and improve AI models for financial use cases such as fraud detection, risk intelligence, chatbots, and voice agents. This role is focused on the full model improvement lifecycle: building datasets, running training and fine-tuning experiments, designing evaluation frameworks, and improving model behavior for high-stakes financial applications.

You will work on models and AI systems that help detect suspicious activity, interpret transaction and behavioral signals, support risk investigations, power customer-facing and internal financial assistants, and generate reliable intelligence across financial workflows. The ideal candidate combines strong machine learning fundamentals with hands-on experience in training, evaluating, and iterating on models for domain-specific systems.

What You’ll Do

• Build and curate datasets for fraud detection, anomaly analysis, financial risk modeling, conversational AI, and voice-based financial applications
• Design and manage training, validation, and test splits with strong methodological rigor
• Run fine-tuning, instruction-tuning, and model adaptation experiments for fraud, risk, chatbot, and voice agent use cases
• Develop reproducible pipelines for training, evaluation, versioning, and experimentation
• Create domain-specific datasets using manual, semi-automated, and synthetic data generation methods
• Design evaluation benchmarks and metrics for accuracy, reliability, hallucination behavior, safety, and domain alignment
• Perform systematic error analysis and failure-mode investigation to improve model quality
• Improve model behavior for tasks such as risk classification, anomaly explanation, case summarization, suspicious activity detection, customer support automation, and voice-driven financial interactions
• Work with transactional, behavioral, conversational, and case data to train models that produce reliable and explainable outputs
• Partner with product, engineering, and risk teams to ensure trained models are ready for production deployment
• Help improve internal AI tools and systems used across fraud, operations, support, and risk workflows
• Track emerging research and apply relevant advances in LLMs, fine-tuning, alignment, speech/voice models, and evaluation to financial AI systems

What We’re Looking For

• Strong background in machine learning, NLP, speech, or LLM systems
• Hands-on experience with model training, fine-tuning, and validation workflows
• Strong Python skills and experience with modern ML tooling
• Experience preparing, curating, labeling, and versioning datasets for ML systems
• Ability to design experiments, define meaningful metrics, and interpret results rigorously
• Experience improving model behavior through data iteration, instruction tuning, or prompt-driven training approaches
• Strong analytical skills in error analysis, model evaluation, and iterative improvement
• Ability to translate research ideas into practical model improvements for production systems

Nice to Have

• Experience with fraud detection, AML, financial crime, financial risk modeling, or other financial AI domains
• Experience working with financial, transactional, behavioral, conversational, or voice datasets
• Experience with LLM fine-tuning, instruction tuning, preference optimization, or alignment techniques
• Familiarity with hallucination mitigation, domain guardrails, and reliability evaluation
• Experience building evaluation pipelines for task-oriented, conversational, decision-support, or voice-based systems
• Familiarity with multimodal datasets such as voice, text, and structured inputs
• Experience with experiment tracking and reproducible ML workflows