REQUIREMENTS

• Experience: 5+ years in AI/ML design and deployment, with at least 3 years working with BFSI clients or solutions.
• Industry Knowledge: Strong understanding of BFSI processes (retail banking, insurance claims, credit risk, anti-fraud systems, regulatory compliance).
• Education: Bachelor’s or Master’s degree
• Other: Excellent communication, stakeholder management and the ability to bridge technology with business needs.

ABOUT THE ROLE

We are looking for an experienced AI Specialist – Deployment Strategist to lead the adoption and deployment of AI-driven solutions in the Banking, Financial Services and Insurance (BFSI) sector. The ideal candidate will bring hands-on expertise in implementing AI/ML models, combined with strong industry knowledge of BFSI processes such as credit risk, fraud detection, customer engagement, claims management and digital banking transformation.
This role requires a balance of technical proficiency and strategic insight to ensure AI deployments deliver measurable business outcomes while meeting regulatory and compliance standards.

KEY RESPONSIBILITIES

• AI DEPLOYMENT & INTEGRATIONLead the end-to-end deployment of AI/ML models into BFSI environments (core banking, insurance platforms, digital channels).Ensure scalability, performance and compliance of AI solutions within regulated financial ecosystems.Work with data science and engineering teams to integrate models into existing BFSI workflows.
• STRATEGY & BFSI ALIGNMENTPartner with banks and insurers to define AI adoption roadmaps aligned to business priorities.Provide strategic guidance on AI use cases in risk management, customer experience, compliance automation and fraud prevention.Translate AI capabilities into tangible value for BFSI stakeholders.
• GOVERNANCE & CONTINUOUS IMPROVEMENTEnsure AI deployments meet industry standards for transparency, fairness and regulatory complianceEstablish monitoring frameworks for model performance and reliability.Drive continuous optimization through real-time feedback and retraining cycles.