• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.

• Design, build, and maintain LLM-powered solutions including custom GPTs and domain-specific assistants. • Develop and optimize prompts, system instructions, and context strategies for accuracy and reliability. • Implement prompt chaining, tool calling, and basic agentic workflows. • Build and support Retrieval-Augmented Generation (RAG) pipelines using vector databases. • Evaluate, test, and improve LLM outputs using defined quality and safety criteria. • Apply responsible AI principles, safety guardrails, and governance standards in all implementations. • Develop full stack AI-enabled applications including backend services, APIs, and UI integrations. • Integrate LLM capabilities into existing enterprise applications. • Build reusable services and components to support AI features. • Debug and resolve application or AI-related issues impacting functionality or user experience. • Develop and deploy AI solutions on Azure and/or AWS. • Support CI/CD pipelines for application and AI workloads. • Assist with MLOps workflows including model versioning, deployment, and monitoring. • Ensure solutions are secure, reliable, and cost-aware in production environments.

Role Description We're looking for a Forward Deployed Engineer who owns customer outcomes, not just code. This is a hybrid role combining software engineering, AI, data engineering, product thinking, and customer collaboration. You'll work directly with enterprise customers, build production-grade integrations and AI workflows, rapidly deliver proof-of-concepts, and turn customer needs into scalable platform capabilities. If you enjoy solving ambiguous problems, building production systems, and seeing your work create immediate business impact, you'll thrive here. What You'll Do - Customer Delivery: - Own enterprise customer implementations from discovery through production deployment - Work directly with customer teams to understand business challenges and define technical solutions - Build production-ready AI workflows, integrations, and data pipelines - Product Engineering: - Develop scalable integrations, APIs, automations, and AI-powered workflows - Build features that initially solve customer-specific needs before evolving into core platform capabilities - Continuously improve platform scalability through automation - Data & AI: - Design and implement data pipelines and integrations - Build evaluation frameworks for AI systems - Ensure outputs remain explainable, reliable, and production-ready - Customer Success: - Build rapid Proofs of Concept (PoCs) for enterprise prospects - Help customers achieve measurable ROI quickly - Translate technical concepts into business value for non-technical stakeholders Qualifications - 5+ years of software engineering experience - Strong Python and SQL skills - Experience building production software end-to-end - Strong data engineering background (ETL, pipelines, data integrations) - Customer-facing experience working directly with enterprise clients - Excellent communication and stakeholder management skills - Comfortable working independently in ambiguous environments - Strong ownership mindset with a focus on customer outcomes Requirements - Nice to Have: - Forward Deployed Engineer, Customer Engineer, Solutions Engineer, or Product Engineer experience - Experience with Databricks, Spark, Snowflake, or dbt - Experience building AI/LLM applications (LangChain, LangGraph, RAG, Vector Databases) - Startup or founding engineer experience - FinTech or enterprise SaaS background Benefits - Competitive salary - Meaningful equity - Fully remote work within the European Time Zone - Employer of Record (EOR) employment - High ownership and direct customer impact - Opportunity to work with cutting-edge AI technologies - Collaborative, high-performance engineering culture - Career growth in one of Europe's fastest-growing AI fintech companies