Descripción de la oferta
Altimetrik Poland is a digital enablement company. We deliver bite-size outcomes to enterprises and start-ups from all industries in an agile way to help them scale and accelerate their businesses. We are unique in Poland's IT market. Our differentiators are an innovation-first approach, a strong focus on core development, and an ability to attack the challenging and complex problems of the biggest companies in the world.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.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 actually 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, procurement — translating their language into technical solutions and back Build reusable patterns — after solving a specific problem, extract the reusable pieces (prompts, retrieval patterns, UI components, evaluation harnesses) into shared assets other FDEs can leverage Own the outcome, not just the code — if the business user isn't getting value, the job isn't done regardless of whether the code is deployed Must-Have Technical Skills Full-stack AI application development — you can build a working end-to-end system with a UI, backend, and AI/LLM integration in weeks, not months Python + FastAPI/Flask for backend services, Streamlit / Databricks Apps / lightweight React for user-facing interfaces Direct hands-on with at least one of: Snowflake Cortex (Analyst/Search/Agents/LLM Functions) OR Databricks Genie (Genie Spaces, semantic models) — you know these products well enough to configure, tune, and integrate them into vertical solutions RAG pipeline construction — chunking, embeddings, vector search (Pinecone, pgvector, Chroma, FAISS, Azure AI Search, Snowflake Cortex Search), retrieval evaluation, grounding, citation LLM application frameworks — LangChain, LangGraph, LlamaIndex, or equivalent — with production usage, not tutorials Prompt engineering with evaluation discipline — you know how to design prompts, evaluate them against ground truth, iterate based on hallucination and accuracy metrics Cloud data platform fluency — Snowflake and/or Databricks at working depth, plus at least one cloud provider (Azure preferred given the Novartis environment, AWS/GCP acceptable) SQL and data modeling — enough to work directly with governed datasets and semantic models Git, CI/CD, and modern development workflows — you own the deployment path, not just the local development API design and integration — you can integrate your solution into existing enterprise systems (SAP, Workday, Coupa, ERP, etc.) via REST APIs Basic MLOps awareness — you understand model versioning, prompt versioning, evaluation harnesses, observability (LangSmith, Datadog, Application Insights) — enough to hand off your solution to the MLOps team cleanly Must-Have Non-Technical Skills Direct customer/stakeholder communication — you can sit in a room with a CFO office user, understand what's frustrating them, and translate that into a technical roadmap without needing a business analyst intermediary Ambiguity tolerance — you're comfortable starting work with a vague problem statement and refining it through prototypes rather than requiring detailed specs upfront Product-shaped thinking — you optimize for user adoption and business outcome, not for elegant architecture or full feature completeness Speed-to-first-demo mindset — you'd rather ship a rough working prototype in Week 1 than a polished spec in Week 4 Willingness to write throwaway code — you know when to build for permanence and when to build for a demo; you don't over-engineerChange management sensibility — you understand that adoption requires more than good technology, and you're willing to do the user-training and hand-holding work to make solutions stick Nice to Have Pharma, life sciences, or CPG Finance domain experience — familiarity with FP&A processes, financial consolidation, regulatory reporting, cost allocation, or clinical trial finance Veeva CRM, IQVIA, SAP S/4HANA, SAP BW, Oracle Financials, Workday Adaptive or similar enterprise Finance tooling Regulated environment delivery — SOX, GxP, data residency, audit trails Snowflake Cortex certification, Databricks certification, or Azure AI Engineer Associate (AI-102) Prior FDE, Solutions Engineer, Sales Engineer, or Field Engineer experience at Palantir, Snowflake, Databricks, OpenAI, Anthropic, or similar Startup / small-team experience — you've had to wear multiple hats and ship end-to-end without organizational scaffolding Direct experience with agentic workflows (multi-agent orchestration, human-in-the-loop, tool-calling) in production, not just demos 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, tax reporting Familiarity with common Finance data — general ledger, cost centers, profit centers, chart of accounts, hierarchies, allocations, KPIs (revenue, gross margin, EBITDA, OPEX, working capital, DSO, DPO) Ability to speak Finance's language — variance analysis, forecasts vs actuals, budget vs actual, trend analysis, drill-through, drill-down, scenario planning Comfort with governed enterprise data — understanding why Finance data has to be trusted, auditable, and lineage-tracked, and why "just run an LLM on the raw data" is not an acceptable answer in a regulated environment.
