Forward Deployed Financial Engineer - Applied AI

We are hiring for two focus areas on this team. This posting is for the Applied AI focus. If you are stronger in deployment, infrastructure and platform engineering, see the Platform focus posting for the same role.

As a Forward Deployed Financial Engineer, you will lead the discovery and delivery of Datarails' AI transformation engagements for existing customers.

After a customer completes implementation, you will work closely with their team to understand how they operate, identify where AI can create meaningful value, and determine what Datarails can build with them. You will develop a comprehensive recommendation and then take an active role in bringing that vision to life.

The solutions may include AI skills, routines, applications, agents, or workflows. This is a highly hands-on role for someone who enjoys working directly with customers, navigating ambiguity, and building practical solutions that solve real business problems.

In the Applied AI focus, you are the engineer who takes on the engagements where the AI itself is the hard part: agents that reason over financial data, systems that chain steps and check their own work, and applications that put those systems in front of a finance team. You set the bar for how the team builds and evaluates AI systems.

You will also serve as an important bridge between customers and our R&D and Product teams, helping translate customer needs into scalable capabilities and influencing the future of the Datarails platform.

• →Help customers answer: What do we need, and how can we build it together?
• →Lead discovery sessions with customers to understand their processes, challenges, goals, and AI transformation opportunities.
• →Develop comprehensive recommendations and solution plans for customer AI transformation projects.
• →Design, build and ship customer-facing solutions, including AI agents, skills, routines, workflows and applications, on top of Datarails, Claude and the customer's connected systems.
• →Define how the solutions you build are tested and evaluated, so the team knows they work before a customer relies on them.
• →Take ownership of projects from initial discovery and recommendation through development and delivery.
• →Work directly with customers throughout the engagement, translating business requirements into practical technical solutions.
• →Engage customers in change management, helping usher their teams into a more AI-native way of working.
• →Build prototypes and products that address immediate customer needs while identifying opportunities for broader reuse.
• →Partner closely with Datarails Customer Success, Product and R&D teams to troubleshoot challenges, communicate customer requirements, and influence product development.
• →Identify recurring customer needs that could evolve into scalable platform capabilities.
• →Clearly explain technical concepts, tradeoffs, and recommendations to both technical and non-technical stakeholders.
• →Document solutions, learnings, and repeatable approaches that can improve future customer engagements.

Two things come first, whatever the focus:

• You are good in front of the customer. You can run discovery with a finance team, explain a tradeoff to a controller, and hold the room when the plan changes. Half of this job is that.
• You ship. You own a problem from the first conversation through to a working solution the customer actually uses.

Then three technical areas, weighted in this order for the Applied AI focus:

1. Applied AI. You have built and shipped LLM-based systems for real users: agents, tool use, retrieval, prompt and context design, evaluation. You know where these systems break and how to make them dependable.
2. Marry the Business Value with the AI Use Case. You understand and continue growing in the business domain so you can bridge the AI solution with what matters to the customer
3. Deployment. You can get what you build running somewhere other than your laptop: APIs and integrations, a cloud environment, authentication.

Also:

• Production experience in Python or TypeScript, with SQL and REST APIs.
• Hands-on experience with modern LLM tooling: agent frameworks, Model Context Protocol, tool use, evals.
• Impact obsessed: driven to deliver measurable outcomes for the customer, and to capture and market those results forward.
• Ability to translate ambiguous business challenges into clear technical recommendations.
• A product-oriented mindset and system-level thinking, with an interest in building solutions that may evolve beyond a single customer use case.
• Comfort working independently, managing projects, and taking ownership from discovery through delivery.
• Ability to collaborate effectively with Product, R&D, Customer Success, and other cross-functional teams.
• Curiosity, adaptability, and a willingness to experiment, learn quickly, and solve problems creatively.

• Forward deployed engineering, hands-on technical consulting, or early engineering roles with broad implementation responsibility.
• Applied AI or ML engineering: you built AI features into a product and want to work directly with the customers who use them.
• Finance, accounting, FP&A, Excel-based workflows, or integrations with ERP and other business systems. You will learn the Office of the CFO quickly here; you do not need to arrive from it.