• Lead the architecture, design, and engineering delivery of the organization's agentic AI platform and related solutions • Drive the transformation of existing products, processes, and workflows into scalable agentic AI-enabled capabilities • Oversee end-to-end AI engineering operations, including development, deployment, monitoring, reliability, and continuous improvement • Partner with cross-functional stakeholders to define technical roadmaps, platform standards, and solution priorities aligned to business goals • Establish engineering best practices for AI system performance, scalability, security, governance, and maintainability

Role Description We're launching a program for experienced engineers transitioning into system architecture roles. The curriculum is being built by a team of senior authors. What we need now is the person who brings it to life for students: - Host the live sessions - Review the work - Run the architecture review boards - Set the technical bar for the people supporting alongside you This is a teaching and review role. You'll be the senior technical presence the cohort learns from week to week: - Run live sessions and office hours - Give real architectural feedback on student deliverables - Act as the escalation point for a team of supporting instructors Your audience: experienced engineers leveling up to system architecture roles. Many already write production code daily. They will spot shallow feedback instantly, so the bar here is real seniority, not familiarity. Format: Async-first with scheduled live sessions. Live sessions and office hours are timed around a US-based cohort. Estimated 15 to 25 hrs/week. Please submit all resumes or CV's in English. Qualifications - 5+ years as a senior software/systems architect, platform engineer, or senior backend/infrastructure engineer - Has designed and shipped systems at scale in a real company, not just coursework or side projects - Depth across the architectural spine: system design, API and service architecture, cloud and infrastructure (AWS, Kubernetes, Terraform, CI/CD), and distributed systems - Can explain why a decision was made, not just how it was implemented, and can diagnose and critique someone else's architecture live - Strong technical communication: comfortable leading a live session and writing clear, specific review feedback - Strong English C1+ — instruction and review are in English for a US-based audience - Comfortable using AI tools in day-to-day technical work Requirements - Host weekly live sessions across the program, focused on the design and decision layer - Run group office hours and 1:1 sessions for students working through projects and the capstone - Review student projects against rubrics: the architecture dossiers, deployed backends, cloud infrastructure, performance reports, security reviews, and the final distributed-system capstone - Give architectural feedback, not just pass/fail checks - Run the mock architecture review boards and the simulated executive-panel presentations - Set the technical standard for a team of supporting instructors Benefits - Milestone-based payments tied to deliverables - Fully remote work with flexible hours - A digital office — we use modern tools (Notion, Miro, Slack, Figma) to keep collaboration smooth - Professional trust and autonomy — no micromanaging - The chance to see real impact: our metric is graduates landing jobs in tech - Join a diverse, international, and close-knit team excited to work with you

Role Description An AI Development Engineer is responsible for solving complex engineering problems by using AI to improve speed, quality, and cost efficiency. This role reimagines traditional software engineering for an AI-augmented world — moving from coding-heavy execution to system orchestration, structured thinking, validation, and agentic application development. The ideal candidate is a strong hands-on engineer who can work with AI tools, foundation models, multimodal systems, and research-driven implementation to build reliable, scalable, production-ready AI applications. Key Responsibilities - Translate ambiguous product or business requirements into clear technical specifications, system designs, and validation plans. - Build, maintain, and scale AI-powered production systems and agentic applications. - Design workflows where LLMs, VLMs, tools, APIs, retrieval systems, and traditional microservices work together reliably. - Use AI-assisted development tools while maintaining code quality, security, performance, and long-term maintainability. - Prevent AI-generated code bloat, dead code, and unnecessary complexity. - Build observability, evaluation, testing, and monitoring systems for AI-generated outputs. - Optimize prompts, model selection, workflows, and inference cost. - Apply Responsible AI principles including safety, privacy, fairness, transparency, and accountability. - Work with cross-functional teams across product, engineering, security, data, and design. - Mentor engineers to move from implementation-heavy development to system orchestration and AI-first engineering practices. Qualifications - Strong software engineering fundamentals and hands-on experience building production-grade systems. - Experience in one or more modalities such as: - Vision/images - Video - Text - Audio - Speech - Deeper knowledge and hands-on experience with LLMs, VLMs, multimodal AI systems, and foundation model architectures. - Ability to actively find, read, understand, and implement ideas from AI research papers. - Experience building or integrating agentic applications, AI copilots, RAG systems, workflow automation, or multimodal GenAI systems. - Strong understanding of system design, APIs, data flows, testing, observability, security, and production reliability. - Practical experience with prompt engineering, context engineering, model evaluation, benchmarking, and AI workflow orchestration. - Ability to convert ambiguous requirements into structured specs, implementation plans, and validation frameworks. - Strong debugging, problem-solving, communication, ownership, and learning agility. Benefits - Best-in-class salary: We hire strong talent and compensate accordingly. - Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners. - Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development. - High-impact work: Build AI-first systems and products used at scale by global clients.

Role Description We’re looking for an AI Engineer for our holding company, Miroma, to support the workflows and the development of AI for our group of agencies. This role will work closely with the innovation team to deliver scalable AI solutions. - Design, build, test, and deploy production-grade AI solutions using Python, TypeScript/JavaScript, and cloud platforms such as Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure. - Take complete ownership of AI use cases, from initial technical design and architecture through to deployment, monitoring, and iteration. Translate ambiguous business needs into robust and scalable technical systems. - Engineer complex integrations with a diverse ecosystem of modern APIs, including leading AI models, cloud data platforms, and enterprise SaaS applications across departments like Marketing, HR, Legal, and Finance. - Identify and abstract common patterns to build robust, reusable components and engines. Your work will establish a foundation that accelerates future development and scales our impact across the organization. - Provide critical production support by rapidly troubleshooting, debugging, and resolving integration issues and system failures to ensure high availability and performance. Qualifications - At least 3+ years of professional experience in a full-stack development role with a verifiable portfolio of shipped projects. - Strong, hands-on proficiency in both Python and TypeScript/JavaScript. - Experience with a major cloud provider (AWS, Azure, GCP). Hands-on experience with Google Cloud Platform (GCP), particularly serverless components like Cloud Functions and Firestore, is strongly preferred. - Experience integrating a variety of third-party REST and/or GraphQL APIs, including expertise in handling complex authentication flows and data transformation. - Experience with LLM API integrations (e.g., Gemini, OpenAI, Anthropic). This must include practical application of prompt engineering, function/tool calling, and parsing complex model responses. - A track record of delivering complete solutions with a high degree of autonomy and minimal supervision. - A mindset of quality ownership, demonstrated by writing clean, maintainable, and production-ready code. Candidates must have proven experience defining and implementing their own unit and integration tests. - Familiarity with workflow automation tools is preferred. Company Description Grounded in a talent-centric mindset, Maker Lab is a marketing agency designed to build decentralised technology-led agency teams, rooted in collaboration with clients from the ground up. Maker Lab’s cross-functional teams of multidisciplinary specialists are embedded into client teams across digital marketing competencies — from measurement & analytics to content production.