Forward Deployment Engineer with AI

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-engineer
• Change 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.