We are seeking a forward-thinking and versatile RPA Developer – Data Scientist to lead the development of advanced analytics, machine learning and intelligent automation initiatives. This role blends traditional data science with low-code automation, GenAI innovation, and AI governance, helping the organization transform both decision-making and operations. The successful candidate will not only build models and automation flows but also contribute to defining our enterprise AI governance framework, policy and risk controls, and secure AI infrastructure.

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You will work cross-functionally with departments such as Operations, Finance, Risk, Compliance, Legal, Retail/ Corporate Banking to align AI use with business strategy, ethical standards, and regulatory expectations.

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RPA, Automation & Intelligent Document Processing (IDP)

• Build and manage Power Automate cloud flows, desktop flows (RPA), and IDP solutions using AI Builder or Azure Form Recognizer.
• Automate business workflows and document processing across Operations, Finance and Risk.
• Integrate GenAI services (e.g., ChatGPT Enterprise, Custom GPT, Azure OpenAI or Copilot Studio) into automation scenarios.

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AI & ML Model Development

• Develop and maintain machine learning and GenAI-powered models for prediction, classification, document generation, and summarization use cases.
• Apply best practices in feature engineering, training, evaluation, and model retraining strategies.
• Explore and implement domain-specific applications of large language models (LLMs), including prompt engineering and fine-tuning.

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AI Governance & Policy Design

• Collaborate with IT, Legal, DPO, and Compliance to establish the organization's AI governance framework.
• Define AI/ML policies, risk assessment procedures, and usage guidelines to ensure responsible and ethical AI deployment.
• Document model development lifecycle, from data sourcing to risk evaluation and audit trails.
• Monitor usage of AI systems to ensure alignment with DORA, GDPR and governance controls.

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Secure AI Infrastructure & Enablement

• Contribute to the design of a secure, scalable AI infrastructure, enabling both in-house model training and safe API-based GenAI usage.
• Work with Engineering and IT Security to set up sandboxes, isolated runtime environments, and controlled data access mechanisms.
• Support integration of AI tools into Microsoft ecosystem (e.g., Azure AI Services, Power Platform, M365 Copilot).

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Collaboration & Business Partnering

• Act as a subject matter expert and enabler for AI initiatives across departments.
• Translate business requirements into data-driven and automation-enhanced solutions.
• Guide internal teams in applying GenAI and automation tools effectively and responsibly.

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Documentation & Reusability

• Maintain structured documentation for models, automation flows, and governance artifacts.
• Ensure version control and reproducibility using Git-based workflows.
• Promote reusability of developed assets across business domains.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field (Must have)
• At least 3+ years of experience in Automation, Data Science & Machine Learning (Must have)
• At least 3+ years of working experience in the banking domain, with familiarity in financial product data (Must have)
• Proven expertise in developing Power Automate flows (cloud, desktop) and IDP solutions using AI Builder or similar tools (must have)
• Practical experience in GenAI model usage and prompt engineering, ideally with LLMs such as OpenAI or Azure OpenAI
• Hands-on experience with ChatGPT Enterprise and the creation of Custom GPTs tailored to internal business use cases
• Strong programming skills in Python and SQL; experience with model development and automation scripting
• Familiarity with AI governance concepts and experience in defining policies, risk frameworks, or compliance documentation
• Experience with Microsoft Azure ecosystem (e.g., Azure ML, Azure OpenAI) is a plus
• Understanding of version control (Git), DevOps/MLOps pipelines, and secure software development practices
• Effective communication skills to work with business, compliance, and engineering teams
• Strong ethical mindset, analytical thinking, and willingness to drive responsible AI adoption

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