Senior Applied AI Engineer - Brandlight

MSc or PhDin Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field. 5+ years of experience in data science or applied machine learning, plus2+ years in ML engineering or software engineering, with direct responsibility for deploying and maintaining production systems. Proven ownership of significant AI products or features.You have been a primary technical driver, taking ambiguous problems from initial concept to a working product used by real customers. Strong foundations in machine learning and statistics, including experimental design, model evaluation, and practical experience with NLP, embeddings, clustering, or related methods for analyzing unstructured data. Strong Python, SQL and Typescript skills, alongside solid software engineering practices: modular architecture, automated testing, version control, code reviews, and maintainable production code. Hands-on experience building LLM-powered applications or AI agents beyond the prototype stage, including tool calling, structured outputs, context management, and systematic evaluation. Experience deploying and operating systems in a cloud environment, including containerization, CI/CD pipelines, logging, monitoring, and debugging production issues. Strong product judgment and independent execution.You can define milestones, prioritize experiments, communicate technical trade-offs, and collaborate effectively across product, engineering, and business teams in a fast-moving environment.

Own AI capabilities end to end.Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement. Develop and improve our core algorithms. Build production-grade AI agents -performance marketing, market research agents, auto-ML agents. Turn research into maintainable software.Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices—not just standalone prototypes. Own quality and performance in production.Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow. Drive technical decisions and execution.Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity. Provide hands-on technical leadership.Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates. MSc or PhDin Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field. 5+ years of experience in data science or applied machine learning, plus2+ years in ML engineering or software engineering, with direct responsibility for deploying and maintaining production systems. Proven ownership of significant AI products or features.You have been a primary technical driver, taking ambiguous problems from initial concept to a working product used by real customers. Strong foundations in machine learning and statistics, including experimental design, model evaluation, and practical experience with NLP, embeddings, clustering, or related methods for analyzing unstructured data. Strong Python, SQL and Typescript skills, alongside solid software engineering practices: modular architecture, automated testing, version control, code reviews, and maintainable production code. Hands-on experience building LLM-powered applications or AI agents beyond the prototype stage, including tool calling, structured outputs, context management, and systematic evaluation. Experience deploying and operating systems in a cloud environment, including containerization, CI/CD pipelines, logging, monitoring, and debugging production issues. Strong product judgment and independent execution.You can define milestones, prioritize experiments, communicate technical trade-offs, and collaborate effectively across product, engineering, and business teams in a fast-moving environment. Experience as a core technical contributor at a high-growth startup, building new products and scaling them as adoption grows. Experience in advertising technology, marketing analytics, search, information retrieval, ranking, or recommendation systems. Familiarity with agent frameworks and SDKs such as ADK, LangChain, or comparable tooling. Experience building systems where traceability, explainability, approval workflows, and reliable execution are important to customer trust.

MSc or PhDin Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field. 5+ years of experience in data science or applied machine learning, plus2+ years in ML engineering or software engineering, with direct responsibility for deploying and maintaining production systems. Proven ownership of significant AI products or features.You have been a primary technical driver, taking ambiguous problems from initial concept to a working product used by real customers. Strong foundations in machine learning and statistics, including experimental design, model evaluation, and practical experience with NLP, embeddings, clustering, or related methods for analyzing unstructured data. Strong Python, SQL and Typescript skills, alongside solid software engineering practices: modular architecture, automated testing, version control, code reviews, and maintainable production code. Hands-on experience building LLM-powered applications or AI agents beyond the prototype stage, including tool calling, structured outputs, context management, and systematic evaluation. Experience deploying and operating systems in a cloud environment, including containerization, CI/CD pipelines, logging, monitoring, and debugging production issues. Strong product judgment and independent execution.You can define milestones, prioritize experiments, communicate technical trade-offs, and collaborate effectively across product, engineering, and business teams in a fast-moving environment.