• Design, build, and operate AI systems that solve real business problems across Finom • Move comfortably from prototype to production: shaping the solution, building the system, measuring quality, and improving it over time • Work on high-impact initiatives across onboarding, customer support, AI accounting, fraud and risk workflows, document understanding, internal automation, and agentic systems • Deliver production-grade AI capabilities that create clear value for customers and the business

Role Description We are looking for a Senior AI Engineer to join our LATAM team in a remote, full-time role. This position is focused on designing, building, and deploying production-ready AI systems that create meaningful impact for our clients. The ideal candidate will work across the full stack of modern AI delivery, including: - RAG pipelines - Agentic frameworks - LLM-powered solutions - Evaluation design - MLOps/LLMOps This role requires a senior technical voice who can lead end-to-end delivery, mentor other engineers, communicate effectively with stakeholders, and help clients make pragmatic decisions about what AI systems should and should not attempt. As part of this role, you will be responsible for: - Leading AI project delivery end to end, ensuring clear governance, strong stakeholder communication, and reliable execution. - Designing and building robust RAG systems, agentic frameworks, and LLM-powered solutions suitable for production environments. - Applying advanced prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts. - Leading feasibility assessments to determine the right technical approach, including prompting, RAG, fine-tuning, classical ML, or hybrid solutions. - Designing evaluation frameworks for AI systems, including LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go gates. - Running structured experiments across prompts, retrievers, chunking strategies, embeddings, reranking approaches, and models. - Identifying and categorizing model failures such as hallucinations, retrieval misses, instruction-following errors, and quality regressions. - Building scalable inference infrastructure and CI/CD pipelines for AI and ML models. - Automating the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, retraining, and continuous improvement. - Designing APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and scalability. - Mentoring junior engineers and contributing to proposals, solution design, and new business initiatives. Qualifications - 6+ years of experience building and deploying AI, ML, or data-driven solutions in production environments. - Strong expertise in Python and solid Git practices. - Hands-on experience with LLM-powered solutions, RAG systems, and modern GenAI development patterns. - Practical experience with RAG components, including chunking, embeddings, retrieval, reranking, and evaluation. - Strong understanding of prompt engineering techniques, including structured outputs, few-shot prompting, instruction design, and tool/agent prompts. - Proven experience designing evaluation strategies for AI systems, including metrics, dataset curation, structured experimentation, and quality gates. - Experience with MLOps/LLMOps practices and tools such as MLflow, Weights & Biases, or similar platforms. - Solid cloud experience with AWS, Azure, or GCP. Azure experience is preferred. - Experience with containerization, orchestration, scalable inference, APIs, and microservices. - Understanding of event-driven architectures and production-grade engineering practices. - Ability to communicate clearly with engineering teams, senior stakeholders, and clients. - A pragmatic approach to AI delivery, balancing innovation, reliability, cost, latency, and business value. Requirements - At least 6 years of professional experience building and deploying AI, ML, or software solutions in production environments. Benefits - 🍔 Every day lunches! (headquarters): Vegetarian, vegan, gluten and sugar free options. Gourmet meals every Friday with our on-site chef! - ⚖️ Flexible working options to help you strike the right balance. - 👨🏽‍💻 All the equipment you need to harness your talent (Macbook and accessories). - ☕ Snacks and beverages available everyday (headquarters). - 🎮 After office events, football, tennis and game nights (headquarters). - ⚽️ Everyone is welcome to join our football league every Wednesday’s and Friday’s. - 📚 Learning opportunities: AWS Certifications (we are AWS Partners), study plans, courses and other certifications, English Lessons, learn from your teammates on our Tech Tuesdays! - 👩‍🏫 Mentoring and Development opportunities to shape your career path. - 🎁 Anniversary and birthday gifts. - 🏡 Great location and even greater teammates!

• Drive complex AI engineering workstreams across multiple business areas, use cases, products, or stakeholder groups. • Define AI engineering approaches that align business goals, workflow opportunities, orchestration patterns, integration requirements, and measurable implementation outcomes. • Translate ambiguous stakeholder needs into structured implementation plans, workflow logic, prompt orchestration, retrieval patterns, tool-calling approaches, and solution recommendations. • Guide the implementation of AI-enabled applications, agents, assistant-style experiences, and workflow components that connect models to practical business processes. • Establish and reinforce best practices for prompt orchestration, tool use, context handling, retrieval flow, API integration, workflow composition, response processing, testing, and code quality. • Design and improve reusable implementation patterns for use cases such as summarization, extraction, question answering, search augmentation, conversational support, workflow automation, grounded insight delivery, and agent-assisted task execution. • Contribute to delivery planning, prioritization discussions, and quality standards across AI engineering engagements. • Mentor, coach, and support practitioners through feedback, guidance, and performance development. • Review deliverables for clarity, technical rigor, quality, consistency, safety, and business usefulness. • Help teams improve workflow reliability, integration quality, testing discipline, and implementation reuse across projects. • Collaborate with AI Scientists, Data Scientists, Data Engineers, Analytics Engineers, and Architects to align AI solutions with business needs, governed data access, platform realities, and technical constraints. • Contribute to hiring, onboarding, capability development, and team maturity within the AI engineering practice. • Follow established governance, privacy, safety, and responsible AI-use standards across the work of the team.

• Leading the architecture, design, and delivery of distributed cloud-native applications capable of high concurrency and demanding real-time data needs. • Designing and building production-grade data pipelines and ETL/ELT workflows — modeling data for both transactional (OLTP) and analytical (OLAP) use, and orchestrating them with modern workflow tooling. • Integrating ML and GenAI capabilities into product features — from model serving and evaluation to LLM-powered enrichment, retrieval (RAG), and intelligent automation within our services. • Championing data quality and correctness — building validation, observability, and testing into every step of the pipeline so downstream analytics and AI features can be trusted. • Collaborating with data science, product, and engineering partners to ship intelligent, complex product features. • Setting and promoting engineering standards for code quality, security, and operational excellence; nurturing automation and continuous improvement. • Diagnosing and eliminating performance bottlenecks and proactively addressing reliability risks. • Mentoring, reviewing code/architectures, and fostering a culture of rapid learning. • Decomposing legacy systems into SOA/microservices, resolving tech debt, and evolving the architecture for scale. • Taking end-to-end ownership of major initiatives from planning through impact.