Location: EMEA (CET) We are looking for a Senior AI Enablement & Implementation Manager to deliver AI-enabled workflows across internal teams. This role focuses on translating business needs into structured AI use cases, coordinating cross-functional implementation, and ensuring adoption through documentation, training, and governance. You will own the lifecycle of AI initiatives, maintain a centralized AI knowledge base, and ensure solutions are reliable, reusable, and easy to use for both technical and non-technical teams. WHAT YOU'LL DO: - Translate real business problems into clear AI requirements, prompts, and workflows. - Design context and evaluation frameworks so AI outputs are reliable, measurable, and reusable. - Lead AI-enabled projects involving summarization, generation, classification, or decision support. - Own the AI Knowledge Base and Use Case Repository, keeping documentation fresh and preventing duplicate work. - Deliver tailored AI trainings, workshops, and practical sessions for both technical and non-technical teams. - Facilitate hands-on experimentation focused on real operational needs. - Scout new AI tools and models, run quick POCs, and assess their value before wider adoption. - Build adoption roadmaps, track usage metrics, gather feedback, and iterate on workflows. - Support AI Champions, moderate internal AI communication channels, and contribute to a strong AI culture. - 5+ years managing cross-functional projects in digital advertising, marketing tech, or software. - Analytical mindset with the ability to identify system issues and unblock teams. - Proven experience leading AI, automation, or other innovation-focused initiatives within an organization. - Strong facilitation and communication skills — able to tailor discussions for both technical and non-technical audiences. - Familiarity with large language models, creative AI tools, and API-based workflows. - Clear, thoughtful communicator — you write documentation and instructions people actually use and understand. - Engineering background (preferably backend). - Located in the EMEA region, working CET hours. NICE TO HAVE: - Hands-on experience with prompt engineering, Python, or SQL. - Certifications in project management methodologies. - Experience in AdTech, MarTech or Client support area. WHAT WE OFFER: - USD compensation that values your expertise.‍ - Work from anywhere: Fully remote to suit your lifestyle.‍ - 31 days of paid time off: 21 days of annual leave + 10 days of sick leave, because your health and work-life balance matter.‍ - Growth-focused environment: Access to learning resources and clear pathways for growth.‍ - Fun team events: Virtual cooking classes, yoga sessions, team quizzes and more - ‍A culture of trust: We cut the red tape — results over rules always. Open talk, ownership, and getting things done together.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are currently looking to hire an AI Engineer Report automation / AI questionnaire to work with us remotely. What you will be doing: - Designs, develops, and deploys end-to-end artificial intelligence systems with a focus on generative AI, large language models, and computer vision. - Owns the full AI lifecycle independently—from data preparation and model development to packaging, serving, scaling, and monitoring models in production. - Builds scalable AI solutions such as chatbots, copilots, predictive systems, intelligent automation, and vision-based applications, leveraging Snowflake as part of the data and analytics environment. Your duties will also involve: - Artificial Intelligence, Machine Learning, and Deep Learning - Generative AI and Large Language Models (LLMs) - Prompt engineering, fine-tuning, and model evaluation - Computer Vision (image classification, object detection, OCR) - NLP, neural networks, and predictive modeling - Python and AI/ML frameworks (TensorFlow, PyTorch, Hugging Face, LangChain, LlamaIndex) - Packaging, serving, and scaling models using APIs, microservices, and batch or real-time inference architectures - Retrieval-Augmented Generation (RAG) - Snowflake (data modeling, analytics, and AI integrations) - Model deployment, monitoring, and optimization (MLOps) - Cloud platforms and AI APIs - Ability to work independently and take full ownership of AI solutions end to end Qualifications - The ideal experience range for this role is 2 to 3 years. - Strong hands-on experience in AI, Machine Learning, and Deep Learning in production use cases. - Practical experience working with Generative AI and Large Language Models (LLMs). - Experience in prompt engineering, fine-tuning, and evaluating LLM-based solutions. - Solid background in Computer Vision (image classification, object detection, OCR). - Experience with NLP, neural networks, and predictive modeling. - Strong programming skills in Python and experience with frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, or LlamaIndex. - Experience building AI applications such as chatbots, copilots, automation tools, or vision-based systems. - Knowledge of Retrieval-Augmented Generation (RAG) architectures. - Experience packaging and serving models using APIs, microservices, and real-time or batch inference. - Experience working with data platforms such as Snowflake. - Understanding of model deployment, monitoring, and optimization (MLOps). - Familiarity with cloud platforms and AI service APIs. - Ability to work independently and own AI solutions end to end, from data preparation to production deployment.

• Diseñar y construir sistemas de agentes de IA capaces de ejecutar flujos de trabajo autónomos (agentic workflows), orquestando múltiples modelos, herramientas y fuentes de datos para resolver problemas complejos de negocio. • Desarrollar con programación asistida por IA (Claude Code, Cursor, Copilot…), maximizando tu productividad y la del equipo. • Implementar soluciones de IA Generativa en producción : sistemas RAG avanzados, chatbots inteligentes, pipelines de procesamiento de lenguaje natural, generación de contenido y automatizaciones basadas en LLMs. • Integrar la IA en el stack de producto , conectando soluciones inteligentes con el frontend, backend, APIs y plataformas de datos existentes de nuestros clientes. • Experimentar y prototipar rápido : alineado con nuestra filosofía de iteración de alta frecuencia, probarás nuevos modelos, técnicas y arquitecturas para validar hipótesis de crecimiento con IA. • Contribuir a la estrategia de IA de Product Hackers , identificando oportunidades para aplicar inteligencia artificial al Growth de productos digitales y proponiendo nuevas líneas de servicio.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are growing the Financial Assistant team at Plata, building intelligent systems that support users in managing their finances, understanding spending, and interacting with financial products in a simple and intuitive way. This team plays a key role in improving customer experience, engagement, and the overall value of our core products. This is a customer-facing product in a regulated environment — accuracy, safety, and trust are non-negotiable. We use AWS, Go, Python and cloud-based models, yet still flexible to mix ready-made tools and ship our custom solutions. We're all about building systems that deliver real, valuable results for our organization. You will be a key specialist in a cross-functional team, working closely with backend, mobile and other LLM engineers. Challenges that await you: - Work with agentic architectures where needed: tool use, multi-step reasoning, orchestration, and failure recovery - Design and maintain eval infrastructure (offline test suites, golden datasets, regression harnesses) that gives a reliable signal after every change - Work with advanced prompts, set up RAG-oriented data sources for efficient retrieval, and evaluate model outputs to ensure accuracy, relevance, and quality - Implement safety and guardrails: hallucination detection, refusal strategies, factual grounding - Run online and offline experiments (canary, A/B) to validate that changes improve real user outcomes - Own observability from day one: trace LLM calls, monitor quality drift, latency, and cost per session in production - Collaborate with backend engineers and product to ship reliable improvements, review code, and maintain production health Qualifications - Strong classical ML fundamentals, you understand what's happening inside models, not just how to call their APIs - Strong eval mindset: you design the measurement system before writing the first prompt, and treat evals as a first-class engineering artifact - Hands-on experience with search and retrieval: dense/sparse/hybrid, reranking, query understanding — using managed tooling effectively, not necessarily from scratch - Practical experience with agentic architectures: tool use, orchestration, failure recovery - Safety-first thinking: guardrails, content policies, graceful degradation under uncertainty, especially in a context where wrong answers have real consequences - Production ML ownership: observability, latency budgets, cost tracking, regression detection - Expertise in Python and its ecosystem, as well as language- and framework-agnostic mindset to achieve project goals - Familiarity with open-source tools to build and evaluate RAG applications - Excellent communication skills and ability to explain complex technical concepts to a broad audience of stakeholders - B1 or higher English level for effective communication with an international team Requirements - Previous experience delivering business-critical ML/AI-powered applications for customer-facing products - Fine-tuning or distillation of LLMs for production use cases - Experience deploying open-source models (Llama, Mistral) in private/on-premise environments - Experience in fintech, banking, or regulated domains