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.

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. - 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), including 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. - 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.

Role Description We are seeking a highly hands-on AI Engineer who thrives in building real-world, production-ready solutions. This role is focused on designing, developing, and deploying end-to-end AI agent solutions using modern tooling—primarily within the Microsoft ecosystem. You will work in a fast-paced, innovation-driven environment, taking ambiguous business problems and turning them into scalable, pragmatic AI-powered solutions. This is a core engineering role requiring strong ownership, execution speed, and deep hands-on expertise in AI/LLM systems. What You Will Be Doing: - Build and deploy AI agents and multi-agent systems on Azure using composable patterns - Build and consume MCP servers to connect agents to internal data sources, tools, and enterprise workflows - Design orchestration patterns, tool integrations, and RAG pipelines - Develop prompt engineering and evaluation workflows (including automated eval loops) - Build APIs for agent integration with enterprise platforms (M365 Copilot, Teams) - Implement data pipelines for unstructured data (transcripts, support interactions) - Set up CI/CD, responsible AI controls, and production monitoring - Use agentic dev tools (GHC CLI, Claude Code, Cursor, Copilot agent mode) as your primary development workflow - Flex into low-code agent configuration, infrastructure, and data engineering as needed Qualifications - 7+ years of software engineering experience, with at least 3+ years building GenAI/LLM-based solutions in production environments - Strong hands-on experience in AI agent development, including building and deploying agents using platforms such as Azure AI Foundry, Microsoft Agent Framework, or similar - Proven expertise in LLM/GenAI engineering, including prompt engineering, model selection, evaluation, and structured outputs - Advanced proficiency in Python, with experience writing production-quality, scalable, and well-tested code - Experience designing and building RESTful APIs for enterprise-grade applications - Strong understanding of Azure cloud infrastructure, including deployment pipelines, identity, and search services - Experience building RAG pipelines, orchestration patterns, and tool integrations - Hands-on experience working with AI-native developer tools such as GitHub Copilot CLI, Claude Code, Cursor, or similar - Ability to build and manage data pipelines for unstructured data (e.g., transcripts, support interactions) - Experience implementing CI/CD pipelines, monitoring, and responsible AI practices - Demonstrated experience in building real AI systems (not just API integrations) Requirements - Experience with MCP server development or similar agent-tool integration protocols - Exposure to LLM/ML Ops, including evaluation frameworks and automated testing loops - Familiarity with PySpark, NLP pipelines, or large-scale data processing - Experience with Copilot Studio and Power Platform - Knowledge of containerization technologies (Docker, Kubernetes) - Front-end development experience for building AI-integrated user interfaces - Experience integrating AI solutions with enterprise tools such as Microsoft Teams and M365 Copilot - Strong understanding of agent evaluation frameworks and automated evaluation workflows - Full-stack development experience is a plus Benefits - Equal employment opportunities to all employees and applicants for employment - Prohibits discrimination and harassment of any type - Policy applies to all terms and conditions of employment, including recruiting, hiring, and the full employee lifecycle