Senior Value Engineer - Banking

Key Responsibilities

• 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.

Requirements

• 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 with the banking value chain, with the ability to translate operational needs into AI use cases across:
• Lending & Onboarding: Loan Origination, KYC/AML, and Banking & Lending.
• Capital Markets: Sales & Trading Ops, Settlements, Reconciliations, and Custody Services.
• Support & Governance: Finance, Risk & Controls, and Regulatory/Transaction Reporting.
• Expertise in Generative AI Techniques
• Knowledge of RAG, prompt engineering, and multi-agent orchestration used to build high-impact use cases (e.g., intelligent compliance assistants or automated data extraction from complex trade documentation).
• Technical Fluency
• Solid knowledge of Python and common ML/Data libraries (e.g., pandas, LangChain) as well as SQL for handling massive, high-velocity transactional datasets.
• Communication
• Strong presentation skills to both internal and external stakeholders, from technical whiteboarding with IT to formal demos for Banking Directors.

Nice to Have

• Agentic Systems
• Hands-on experience building systems using LLM orchestration and function calling within highly regulated "Data Sovereignty" environments.
• Banking Systems
• Familiarity with FSI-specific platforms (e.g., nCino, Guidewire, FIS, Fiserv, or Bloomberg).
• Cloud Platforms
• Experience deploying models across major cloud providers (Azure AI, AWS Bedrock, GCP Vertex).