Senior Value Engineer - Banking
Senior Value Engineer specializing in Banking who leads technical pre- and post-sales engagements for AI-driven Process Intelligence solutions at major financial institutions. Requires 5+ years in complex Banking/Financial Services environments, deep domain knowledge across lending, capital markets, and regulatory functions, plus technical fluency in Python, SQL, and generative AI techniques.
Key Responsibilities
AI Discovery & Solutioning
- Understand clients' overarching AI strategies and distinct challenges in Banking (e.g., fraud detection, credit risk assessment, 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
- Guide implementation post-sale to ensure efficiency and adoption thresholds are met
Hackathons & Prototyping
- 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
- Scale knowledge across the organization regarding the nuances of financial operations and transaction lifecycles
Requirements
- 5+ years of experience leading technical pre-sales and post-sales engagements specifically within complex Banking or Financial Services environments, including building ROI business cases and guiding technical implementations
- 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
- 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)
- Solid knowledge of Python and common ML/Data libraries (e.g., pandas, LangChain) as well as SQL for handling massive, high-velocity transactional datasets
- Strong presentation skills to both internal and external stakeholders, from technical whiteboarding with IT to formal demos for Banking Directors
- Bachelor's Degree required; Master's Degree in Finance, Computer Science, Economics, or Mathematics preferred
Nice to Have
- Hands-on experience building systems using LLM orchestration and function calling within highly regulated "Data Sovereignty" environments
- Familiarity with FSI-specific platforms (e.g., nCino, Guidewire, FIS, Fiserv, or Bloomberg)
- Experience deploying models across major cloud providers (Azure AI, AWS Bedrock, GCP Vertex)
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