• Design, build, and deploy end-to-end AI-powered product experiences from concept through production. • Architect and implement scalable AI systems leveraging LLMs, embeddings, multimodal models, retrieval systems, agent frameworks, and modern data infrastructure. • Build production-grade multi-agent workflows and orchestration systems using frameworks such as LangGraph, LangChain, Mastra, and custom tooling. • Develop and optimize Retrieval-Augmented Generation (RAG) systems, including embeddings, vector search, retrieval pipelines, chunking strategies, and relevance tuning. • Build multimodal AI workflows that analyze and reason over images, creative assets, and visual datasets using modern multimodal LLMs, embedding models, and specialized tooling such as SAM2/SAM3. • Create AI-assisted experiences for search, discovery, content generation, personalization, and creative workflows across Material Bank’s platform. • Evaluate, refine, and improve AI-generated outputs for quality, tone, accuracy, and creative alignment through testing, iteration, and human-in-the-loop evaluation strategies. • Partner closely with Product, Design, Engineering, Data, and Executive Leadership to identify high-impact opportunities and translate ambiguous ideas into production-ready AI capabilities. • Make architectural decisions that balance speed, scalability, latency, cost, accuracy, and long-term maintainability. • Continuously evaluate emerging AI technologies, models, frameworks, and workflows to identify opportunities that create meaningful business and user value.

Role Description We are seeking a skilled AI/MLOps Engineer to join the innovative team at 99x Brazil. In this role, you will be responsible for designing, deploying, and maintaining scalable machine learning infrastructure and pipelines that enable rapid development and reliable deployment of AI models. You will work closely with data scientists, engineers, and product managers to ensure seamless integration of AI capabilities into production systems. You will play a crucial part in automating ML workflows, monitoring model performance, and optimizing resource utilization in cloud environments. Join us to help drive the future of AI-powered solutions in a fast-paced, collaborative environment. Responsibilities - Design and maintain monitoring and observability solutions for AI applications and ML pipelines - Track logs, metrics, and traces using tools such as CloudWatch, Datadog, or similar platforms - Develop evaluation and testing frameworks for prompts, models, and AI workflows - Perform regression testing and quality validation for LLM-based systems - Manage prompt experimentation, versioning, and A/B testing processes - Debug AI workflows, including model outputs, orchestration pipelines, and infrastructure failures - Support deployment, scaling, and maintenance of AI/ML infrastructure in production environments - Collaborate with engineering and product teams to improve system reliability and performance - Analyze production data and user feedback to drive continuous improvement of AI systems - Contribute to operational best practices, documentation, and incident response processes Qualifications - Experience with DevOps, SRE, MLOps, or AI infrastructure engineering - Strong understanding of monitoring and observability concepts - Hands-on experience with tools such as Datadog, CloudWatch, Grafana, Prometheus, or similar - Experience supporting AI/ML or LLM-based applications in production - Familiarity with prompt engineering, model evaluation, and experimentation workflows - Knowledge of cloud platforms such as AWS, Azure, or Google Cloud - Experience troubleshooting distributed systems and production pipelines - Proficiency in Python, scripting, or automation tooling - Strong analytical and problem-solving skills - Excellent communication and collaboration abilities Nice to Have - Experience with LLM orchestration frameworks - Familiarity with vector databases and RAG architectures - Experience with CI/CD pipelines for ML systems - Knowledge of Kubernetes, Docker, and infrastructure-as-code tools - Experience with AI governance, security, or compliance practices Benefits - Your pick when it comes to employment models: CLT/PJ/Cooperativa - We provide resources for you to grow and learn on the job, including online courses, mentoring, and the latest-gen laptops - A fully remote work environment with flexible working hours - Bonus for any referrals that we end up hiring

• Design, implement, and deploy ML/AI models end-to-end • Maintain and evolve AI systems in production • Bring an AI-forward coding mindset • Partner closely with product, engineering, and data teams • Translate technical tradeoffs into terms non-specialists can act on • Participate actively in code reviews and design discussions • Contribute to AI architecture decisions • Take ownership of meaningful work end-to-end • Raise the bar on engineering practices by supporting junior engineers

• Work directly with key stakeholders in a consultative manner to identify pain points and start building solutions to simplify manual and redundant workflows. • Write the production code — this isn't a strategy role. • Build agents to automate grind. • Partner with function heads to design, test, and deploy AI-driven agents and workflow automations. • Own evals, observability, guardrails, and the 'is this thing actually working' layer. • Use AI coding assistants (e.g. Claude, Replit, Vercel, etc.), low/no-code platforms, and workflow automation tools to rapidly build and ship MVPs. • Own cloud infrastructure, CI/CD, and operational reliability for what you ship. • Lead vendor evaluations, negotiate modular solutions, and ensure clean integration with our data and AI platforms.