Forward Deployment Engineer with AI

Altimetrik; Barcelona; Presencial; EUR 42.000 - 69.000; Jornada completa

Descripción de la vacante

Altimetrik Poland seeks a Forward Deployment Engineer to translate complex Finance problems into working AI solutions. You will deploy in customer environments, iterate rapidly, and drive measurable business value from day one.

You will work closely with CFO office stakeholders, build vertical AI apps on top of Snowflake Cortex or Databricks Genie, and craft reusable patterns for future projects. Strong prototyping, deployment, and governance experience are valued.

Formación

• Full-stack AI application development capability.
• Hands-on with Snowflake Cortex or Databricks Genie.
• Experience building RAG pipelines and vector stores.
• Proficiency with LangChain or similar frameworks.
• Prompt engineering with evaluation discipline.
• SQL and data modeling for governed data.
• Git, CI/CD and modern dev workflows.
• API design/integration with enterprise systems.
• MLOps awareness including versioning/observability.
• Direct customer/stakeholder communication.
• Ambiguity tolerance and rapid prototyping.
• Product-shaped thinking and speed to demo.

Responsabilidades

• Own end-to-end delivery of AI solutions for Finance problems.
• Translate business requirements into working software.
• Build vertical AI apps atop the AI platform.
• Prototype quickly with functional UI/app in weeks.
• Deploy in regulated customer environments.
• Measure usage, adoption, and business outcomes from day one.
• Handle last-mile usability and adoption support.
• Collaborate with CFO office, FP&A, controllership, treasury.
• Create reusable patterns for future engineers.
• Own outcomes, not just the code.

Conocimientos

Full-stack AI

RAG pipelines

LLM apps

Prompt engineering

SQL & data modeling

Git & CI/CD

API integration

MLOps awareness

Customer communication

Ambiguity tolerance

Product thinking

Herramientas

Snowflake Cortex

Databricks Genie

LangChain

Pinecone

Chroma

Descripción del empleo

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
• 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
• 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.