Forward Deployment Engineer with AI

Altimetrik; Cataluña; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Altimetrik Poland is seeking a Forward Deployment Engineer to bridge AI platform capabilities with Finance users. You will translate ambiguous problems into working AI solutions, deploy them into regulated environments, and iterate toward measurable business outcomes.

You will own end-to-end delivery, prototype fast, and collaborate with CFO office teams to ensure adoption and value realization. A strong emphasis on data governance, RBAC, and compliance is expected.

Formación

• Full-stack AI application development with UI, backend and AI/LLM integration
• Backend services using Python frameworks (FastAPI/Flask)
• Experience with Snowflake Cortex or Databricks Genie in vertical solutions
• Knowledge of RAG pipelines, embeddings and vector search
• Familiarity with LangChain/LangGraph or similar frameworks
• Prompt design with evaluation against ground truth and metrics
• Fluency in cloud data platforms (Snowflake/Databricks) and at least one cloud provider
• SQL and data modeling for governed datasets

Responsabilidades

• Own end-to-end delivery of AI solutions for Finance problems
• Translate business needs into working software with stakeholders
• Build vertical AI apps rapidly on top of the platform
• Prototype quickly to demo to business users
• Deploy in customer environments with governance and compliance
• Instrument measurement with usage and outcome telemetry
• Handle data quirks, edge cases and user training
• Collaborate with CFO, FP&A, controllership and treasury teams
• Create reusable patterns and components for reuse by other engineers
• Own the outcome, not just the code

Conocimientos

Full-stack AI

Python

FastAPI/Flask

Streamlit

Databricks Genie

Snowflake Cortex

RAG pipelines

LangChain

Prompt engineering

Cloud data platform

SQL

Git CI/CD

API design

MLOps basics

Herramientas

Snowflake Cortex

Databricks Genie

Streamlit

Databricks Apps

Pinecone

Chroma

FAISS

Azure AI Search

Snowflake Cortex Search

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
• 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.