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.

About the job

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)