Employer description & source details
AI Discovery & Solutioning: Understand the clients' overarching AI strategies and the distinct challenges inherent to Banking (e.g., fraud detection, credit risk assessment, and automating KYC/AML). Translate complex financial workflows into innovative AI solutions that drive measurable ROI. Pre- and Post-Sales Execution: Actively drive the customer lifecycle. Lead technical discovery and capability demonstrations during the expansion cycle. Remain deeply involved post-sale to guide implementation, ensuring efficiency and adoption thresholds are met. Hackathons & Prototyping: Think creatively to tackle heavily siloed legacy core-banking systems. Leverage cutting-edge AI to rapidly build prototypes in client hackathons, solving pain points across Settlements, Reconciliations, and Payments. Agentic Process Transformation: Support enterprise clients in shifting from rule-based automation to autonomous AI agents. Enable "intelligent" banking processes, such as autonomous exception handling in Trade Ops or automated Complaints Management . Proof Projects: Execute end-to-end Business-critical Proof-of-Value projects. Architect secure, scalable LLM/agent systems (with RAG and guardrails) that integrate with complex enterprise stacks like SAP Banking, Salesforce FINS, or core-banking ledgers. Domain Practitioner Leadership: Serve as a functional subject matter expert for the Banking value chain, scaling knowledge across the organization regarding the nuances of financial operations and transaction lifecycles. 5+ years of experience leading technical pre-sales and post-sales engagements specifically within complex Banking or Financial Services environments. This includes building ROI business cases and guiding technical implementations. Strong Domain Knowledge: Deep familiarity
Extracted skills: python, sql, aws, azure, gcp, artificial intelligence, sales, finance, operations