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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Forward Deployment Engineer With Ai - **Company:** Altimetrik - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Analysis of Variance (ANOVA), Application Frameworks, Application Performance Management, ARM Architecture, Microsoft Azure, Continuous Integration, Python (Programming Language), Microsoft Office, Raw Data, Role-Based Access Control, Webui, Salesforce.Com, SAP (Applications), SAP NetWeaver Business Warehouse, Search Technologies, SQL Databases, Management of Software Versions, Datadog, Oracle Hyperion, Enterprise Software Applications, Cloud Platform System, ReactJS, Flask (Web Framework), Large Language Models, Snowflake, Prompt Engineering, Backend, Git, Fastapi, AI Platforms, Material UI, SAP S/4HANA, Drilldown, Coupa Procurement, Machine Learning Operations, Api Design, Restful APIs, Streamlit Framework, Software Version Control, Workday, GXP, Databricks - **Published:** September 7, 2026 - **Apply:** https://www.buscojobs.com.es/forward-deployment-engineer-with-ai-en-barcelona-ID-370306113 ## About the Role 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 ## Description Altimetrik Polandis 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 aForward 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 DoOwn end-to-end delivery of AI solutions for specific Finance business problems- from problem definition with the business stakeholder through deployment, adoption, and measurable outcomeTranslate ambiguous, evolving business requirements into working software- often without a formal spec, working directly with the Finance user who owns the problemBuild vertical AI applications rapidlyon top of the existing AI platform - leveraging Snowflake Cortex, Databricks Genie, RAG pipelines, and agentic workflows to solve narrow, high-value business problemsPrototype 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 feedbackDeploy solutions inside the customer's regulated environment- respecting existing governance, RBAC, data residency, audit, and compliance constraintsInstrument for measurement- every deployed solution ships with usage metrics, adoption tracking, and business outcome telemetry from day oneHandle the \"last mile\" that makes AI actually usable- data quirks, business rule exceptions, edge cases, user training, change management, and adoption supportWork directly with Finance business stakeholders- CFO office, FP&A, controllership, treasury, procurement - translating their language into technical solutions and backBuild reusable patterns- after solving a specific problem, extract the reusable pieces (prompts, retrieval patterns, UI components, evaluation harnesses) into shared assets other FDEs can leverageOwn 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 deployedMust-Have Technical SkillsFull-stack AI application development- you can build a working end-to-end system with a UI, backend, and AI/LLM integration in weeks, not monthsPython + FastAPI/Flaskfor backend services,Streamlit / Databricks Apps / lightweight Reactfor user-facing interfacesDirect 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 solutionsRAG pipeline construction- chunking, embeddings, vector search (Pinecone, pgvector, Chroma, FAISS, Azure AI Search, Snowflake Cortex Search), retrieval evaluation, grounding, citationLLM application frameworks- LangChain, LangGraph, LlamaIndex, or equivalent - with production usage, not tutorialsPrompt engineering with evaluation discipline- you know how to design prompts, evaluate them against ground truth, iterate based on hallucination and accuracy metricsCloud 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 modelsGit, CI/CD, and modern development workflows- you own the deployment path, not just the local developmentAPI design and integration- you can integrate your solution into existing enterprise systems (SAP, Workday, Coupa, ERP, etc.) via REST APIsBasic 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 cleanlyMust-Have Non-Technical SkillsDirect 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 intermediaryAmbiguity tolerance- you're comfortable starting work with a vague problem statement and refining it through prototypes rather than requiring detailed specs upfrontProduct-shaped thinking- you optimize for user adoption and business outcome, not for elegant architecture or full feature completenessSpeed-to-first-demo mindset- you'd rather ship a rough working prototype in Week 1 than a polished spec in Week 4Willingness 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 stickNice to HavePharma, life sciences, or CPG Finance domain experience- familiarity with FP&A processes, financial consolidation, regulatory reporting, cost allocation, or clinical trial financeVeeva CRM, IQVIA, SAP S/4HANA, SAP BW, Oracle Financials, Workday Adaptiveor similar enterprise Finance toolingRegulated environment delivery- SOX, GxP, data residency, audit trailsSnowflake Cortex certification, Databricks certification, or Azure AI Engineer Associate (AI-102)Prior FDE, Solutions Engineer, Sales Engineer, or Field Engineer experienceat Palantir, Snowflake, Databricks, OpenAI, Anthropic, or similarStartup / small-team experience- you've had to wear multiple hats and ship end-to-end without organizational scaffoldingDirect experience with agentic workflows(multi-agent orchestration, human-in-the-loop, tool-calling) in production, not just demosDomain Skills - Finance FocusWorking 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 reportingFamiliarity 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 planningComfort 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. ## Related Videos - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Building the Next Generation of Software](https://www.wearedevelopers.com/videos/100186-building-the-next-generation-of-software) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Why Your AI Tool Fails After the Demo](https://www.wearedevelopers.com/magazine/704-why-your-ai-tool-fails-after-the-demo)