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Salary: USD 120,000 - 150,000 / annual
Datarails is an AI-powered FinanceOS company helping finance teams transform their operations through intelligent automation, AI, and seamless integrations. With ~350 employees globally, the company is building next-generation finance technology.
As a Forward Deployed Financial Engineer (Applied AI focus), 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 their operations, identify where AI can create meaningful value, and determine what Datarails can build together.
You will develop comprehensive recommendations and take an active role in bringing that vision to life. 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 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 finance teams. You will set the bar for how the team builds and evaluates AI systems.
You will also serve as a bridge between customers and R&D and Product teams, helping translate customer needs into scalable capabilities and influencing the future of the Datarails platform.
Key responsibilities include: leading discovery sessions to understand customer processes and AI transformation opportunities; developing comprehensive recommendations and solution plans; designing, building, and shipping customer-facing solutions including AI agents, skills, routines, workflows, and applications; defining how solutions are tested and evaluated; taking ownership of projects from discovery through delivery; working directly with customers to translate business requirements into technical solutions; engaging customers in change management; building prototypes that address immediate needs while identifying broader reuse opportunities; partnering with Customer Success, Product, and R&D teams; identifying recurring customer needs that could become platform capabilities; explaining technical concepts to both technical and non-technical stakeholders; and documenting solutions and repeatable approaches.
Two core competencies come first: you are excellent in front of customers (running discovery with finance teams, explaining tradeoffs, holding the room when plans change), and you ship (owning problems from first conversation through working solutions customers actually use).
Technical areas weighted for 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. Business Value and AI Use Case: You understand the business domain and can bridge AI solutions with what matters to the customer.
3. Deployment: You can get what you build running beyond your laptop (APIs, integrations, cloud environments, authentication).
REQUIREMENTS:
- 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 customers and capture/market results
- Ability to translate ambiguous business challenges into clear technical recommendations
- Product-oriented mindset and system-level thinking, with interest in building solutions that may evolve beyond single customer use cases
- Comfort working independently, managing projects, and taking ownership from discovery through delivery
- Ability to collaborate effectively with Product, R&D, Customer Success, and cross-functional teams
- Curiosity, adaptability, and willingness to experiment, learn quickly, and solve problems creatively
Helpful experience includes: forward deployed engineering, hands-on technical consulting, or early engineering roles with broad implementation responsibility; applied AI or ML engineering where you built AI features into products; finance, accounting, FP&A, Excel-based workflows, or integrations with ERP and business systems (you will learn the Office of the CFO quickly; prior domain experience not required).
