Forward Deployment Engineer with AI

We are looking for a Forward Deployment Engineer (FDE) to sit at the intersection of AI platform capabilities and Finance business users — translating ambiguous business problems into working AI solutions, deploying them into the customer’s environment, and iterating in tight loops until they deliver measurable value.

Quick Facts

• Work 100% remotely or from the Kraków hub.

What You’ll Do

• Own end-to-end delivery of AI solutions for specific Finance business problems — from problem definition with the business stakeholder through deployment, adoption, and measurable outcome.
• Translate ambiguous, evolving business requirements into working software — often without a formal spec, working directly with the Finance user who owns the problem.
• Build vertical AI applications rapidly on top of the existing AI platform — leveraging Snowflake Cortex, Databricks Genie, RAG pipelines, and agentic workflows to solve narrow, high-value business problems.
• Prototype in days, not months — get a functional Streamlit app, Databricks App, or lightweight web UI in front of business users within the first 1–2 weeks of engagement, then iterate based on real user feedback.
• Deploy solutions inside the customer’s regulated environment — respecting existing governance, RBAC, data residency, audit, and compliance constraints.
• Instrument for measurement — every deployed solution ships with usage metrics, adoption tracking, and business outcome telemetry from day one.
• Handle the “last mile” that makes AI usable — data quirks, business-rule exceptions, edge cases, user training, change management, and adoption support.
• Work directly with Finance business stakeholders — CFO office, FP&A, controllership, treasury, and procurement — translating their language into technical solutions and back.
• Build reusable patterns — extract prompts, retrieval patterns, UI components, and evaluation harnesses into shared assets after solving a specific problem.
• Own the outcome, not just the code — ensure business users receive value from deployed solutions.

Must-Have Technical Skills

• Full-stack AI application development: build working end-to-end systems with a UI, backend, and AI/LLM integration in weeks.
• Python with FastAPI or Flask for backend services; Streamlit, Databricks Apps, or lightweight React for user-facing interfaces.
• Direct hands-on experience with Snowflake Cortex (Analyst, Search, Agents, LLM Functions) or Databricks Genie (Genie Spaces, semantic models), including configuration, tuning, and vertical-solution integration.
• RAG pipeline construction: chunking, embeddings, vector search, retrieval evaluation, grounding, and citation.
• Production use of LangChain, LangGraph, LlamaIndex, or equivalent LLM application frameworks.
• Prompt engineering with evaluation discipline, including ground-truth evaluation and iteration based on hallucination and accuracy metrics.
• Working-depth Snowflake or Databricks knowledge plus Azure, AWS, or GCP experience.
• SQL and data modeling for governed datasets and semantic models.
• Git, CI/CD, and modern development workflows, including ownership of the deployment path.
• API design and integration with enterprise systems such as SAP, Workday, Coupa, and ERP platforms via REST APIs.
• Basic MLOps awareness: model and prompt versioning, evaluation harnesses, and observability using tools such as LangSmith, Datadog, or Application Insights.

Must-Have Non-Technical Skills

• Direct customer and stakeholder communication; translate Finance-user frustrations into a technical roadmap without a business analyst intermediary.
• Ambiguity tolerance; refine vague problem statements through prototypes rather than requiring detailed specifications.
• Product-shaped thinking focused on user adoption and business outcomes.
• Speed-to-first-demo mindset; deliver a rough working prototype in Week 1 rather than a polished specification in Week 4.
• Willingness to write throwaway code and avoid over-engineering.
• Change-management sensibility, including user training and adoption support.

Nice to Have

• Pharma, life sciences, or CPG Finance domain experience, including FP&A, financial consolidation, regulatory reporting, cost allocation, or clinical-trial finance.
• Experience with Veeva CRM, IQVIA, SAP S/4HANA, SAP BW, Oracle Financials, Workday Adaptive, or similar enterprise Finance tooling.
• Regulated-environment delivery experience, including SOX, GxP, data residency, and audit trails.
• Snowflake Cortex certification, Databricks certification, or Azure AI Engineer Associate (AI-102).
• Prior FDE, Solutions Engineer, Sales Engineer, or Field Engineer experience.
• Startup or small-team experience delivering end-to-end solutions.
• Production experience with agentic workflows, including multi-agent orchestration, human-in-the-loop workflows, and tool-calling.

Domain Skills — Finance Focus

• Working understanding of Finance business processes: order-to-cash, procure-to-pay, record-to-report, plan-to-report, close cycles, financial planning and analysis, management reporting, statutory reporting, and tax reporting.
• Familiarity with Finance data: general ledger, cost centers, profit centers, chart of accounts, hierarchies, allocations, and KPIs including revenue, gross margin, EBITDA, OPEX, working capital, DSO, and DPO.
• Ability to speak Finance’s language: variance analysis, forecasts versus actuals, budget versus actual, trend analysis, drill-through, drill-down, and scenario planning.
• Comfort with governed enterprise data, including the need for trusted, auditable, lineage-tracked Finance data in regulated environments.