Senior Value Engineer - Banking

Celonis Celonis · Data AI · New York, NY · Value Engineering

This role focuses on applying AI and ML technologies, specifically Generative AI techniques like RAG and multi-agent orchestration, to solve complex business problems in the banking sector. The Senior Value Engineer will work with enterprise clients to understand their AI strategies, translate financial workflows into AI solutions, prototype these solutions using Celonis' platform and AI partners, and demonstrate their value to executives. Key responsibilities include AI discovery, pre- and post-sales execution, hackathons, prototyping, and supporting the shift to autonomous AI agents. The role requires strong domain knowledge in banking, technical fluency in Python and ML libraries, and expertise in Generative AI techniques.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 5+ years of experience leading technical pre-sales and post-sales engagements specifically within complex Banking or Financial Services environments
  • Strong Domain Knowledge: Deep familiarity with the banking value chain, with the ability to translate operational needs into AI use cases
  • Expertise in Generative AI Techniques: Knowledge of RAG, prompt engineering, and multi-agent orchestration used to build high-impact use cases
  • 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.
  • Bachelor’s Degree required

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).
  • Master's Degree in Finance, Computer Science, Economics, or Mathematics

What the JD emphasized

  • complex Banking or Financial Services environments
  • Deep familiarity with the banking value chain
  • Expertise in Generative AI Techniques
  • Agentic Systems

Other signals

  • AI/ML is core to the product
  • AI is used to drive business action
  • AI solutions for banking
  • Agentic Process Transformation