You need to have:

• Proficiency in Python
• 3-4+ years of professional and commercial experience developing AI/ML projects in financial services, banking, fintech, or related regulated industries, with proven expertise in solving business challenges related to compliance, risk management, or fraud prevention
• Strong knowledge and hands-on experience with AI frameworks and vector databases
• Proven experience developing solutions based on large language models (LLMs), Retrieval-Augmented Generation (RAG), building intelligent agents, applying advanced prompting techniques, and other modern AI approaches specifically in banking or financial contexts
• Experience with AML (Anti-Money Laundering) systems, fraud detection models, credit risk assessment, transaction monitoring, or similar financial compliance and risk use cases
• Understanding of AI-powered Customer Risk Rating, KYC/KYB automation, regulatory reporting, and compliance-driven AI applications
• Experience with AI-powered OCR for processing financial documents such as invoices, bank statements, identity documents, contracts, and regulatory forms
• Experience building agentic chatbots and conversational AI for banking customer service, internal compliance queries, or operational workflows
• Experience deploying and managing large language models (LLMs) and Vision-Language Models (VLMs) in both on-premises and cloud environments, particularly Azure
• Familiarity with Azure AI services and frameworks
• Understanding and practical experience with parameter-efficient fine-tuning (PEFT) approaches for large language models
• Familiarity with financial data structures, banking APIs, and integration with core banking systems
• Collaboration skills to work effectively with product managers, compliance teams, risk analysts, and business leaders in regulated environments
• English language proficiency at B2+ level or higher

Wiil be a plus:

• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, Finance, or related fields
• Knowledge of big data platforms (e.g., Databricks, Spark) and cloud services (AWS, Azure, Google Cloud) with experience in secure, compliant deployments for financial institutions
• Deep understanding of transformer architecture at a low level of abstraction
• Full-stack development experience for building end-to-end AI-powered financial applications
• Experience building MLOps processes: automating ML model lifecycle (training, deployment, monitoring), managing data and model versions (MLflow, DVC), configuring pipelines (Kubeflow, Airflow), A/B testing in production environments
• Understanding of model explainability (SHAP, LIME) and fairness in AI, critical for regulated financial services
• Certifications in ML, data science, or cloud platforms
• Experience with financial regulations and compliance frameworks (GDPR, AML directives, PSD2, Basel standards)
• Familiarity with DevOps practices including CI/CD platforms, containerization (Docker, Kubernetes), and Linux fundamentals
• Experience with Git for version control and collaborative development in regulated environments