Forward Deployed Engineer

As a Forward Deployed Engineer on the Koch Capabilities Finance team, you will work directly with finance teams to identify opportunities, build AI-enabled solutions, and deliver measurable business value.

This newly created role will accelerate how finance uses AI, automation, data, and software to improve decision-making, financial stewardship, and operational effectiveness. You will move quickly from problem discovery to working solutions, partnering with finance leaders, subject matter experts, end users, and technology teams throughout the process.

We are looking for an entrepreneurial engineer who is comfortable with ambiguity, uses AI-assisted development tools to increase speed and capability, and wants direct accountability for moving important business opportunities from ideas to adopted solutions. You will lead technical delivery while helping determine the right long-term path for each solution, whether that means scaling, integrating, transitioning ownership, or stopping work that does not create sufficient value. This role is eligible to be worked in Plano, Texas or Wichita, Kansas.

Koch Capabilities Finance is global team that sits at the intersection of finance, technology, transformation, and strategy. We modernize how finance work gets done, improve decision quality, strengthen financial stewardship, scale enterprise capabilities, and create capacity for higher-value business partnership.

Our work spans planning, accounting, reporting, controls, enterprise finance applications, data, AI enablement, process transformation, and capability development. We start with meaningful business problems and combine finance expertise, technology, and new ways of working to create value across Koch.

• Work alongside finance teams to understand their decisions, data, controls, exceptions, and desired business outcomes.
• Identify root causes and determine how AI, automation, analytics, data, or software can improve end-to-end workflows.
• Design, code, integrate, test, and deploy working solutions using real workflows and data while balancing speed, security, reliability, and maintainability.
• Establish success measures, observe how solutions are used, gather evidence, and iterate based on adoption, quality, productivity, risk reduction, capacity creation, and decision impact.
• Build appropriate documentation, tests, monitoring, reusable components, and technical guardrails. Partner with finance and technology leaders to determine the right long-term path for each solution.
• Use AI-assisted development while independently evaluating outputs for accuracy, security, compliance, maintainability, and business risk.
• Create reusable patterns and tools, share learning, and help finance and technology partners identify and deliver future opportunities.

• Experience translating ambiguous business problems into software, automation, data, or AI-enabled solutions.
• Experience working directly with business stakeholders and end users to discover requirements and validate outcomes.
• Experience building and deploying applications using languages such as Python, TypeScript/JavaScript, C#, or similar technologies.
• Experience integrating applications with APIs, databases, enterprise systems, or cloud services.
• Experience applying engineering practices such as testing, security, observability, documentation, and maintainability, including critically reviewing AI-generated code and outputs.

• Experience building solutions for finance, accounting, or other control-sensitive environments.
• Experience with financial processes such as planning, reporting, controls, procure-to-pay, order-to-cash, or audit.
• Experience designing data pipelines, governed data architectures, or analytical solutions using SQL, Snowflake, PostgreSQL, or similar technologies.
• Experience designing generative AI solutions, agents, retrieval architectures, evaluation frameworks, or human-in-the-loop controls.
• Experience creating reusable engineering patterns, tooling, documentation, or guardrails that enable broader adoption.
• Experience scaling a prototype or emerging product into an enterprise solution or durable support model.
• Bachelor’s or master’s degree in computer science, software engineering, data science, finance, accounting, mathematics, or a related discipline, or equivalent practical experience.