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Senior Applied AI Engineer
Brandlight. Translate business and product needs into well-defined problems, research plans, and technical designs .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in machine learning, AI product development, and software engineering, with a strong focus on deploying and maintaining production systems. Proficient in Python, SQL, and TypeScript, with hands-on experience in building LLM-powered applications and implementing robust software practices.
Highest-signal resume keywords
MSc Or PhD In Computer Science5+ Years In Data Science Or Applied Machine LearningStrong Foundations In Machine Learning And StatisticsExperience Deploying And Operating Systems In A Cloud EnvironmentHands-On Experience Building LLM-Powered Applications
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Machine LearningStatisticsNatural Language ProcessingPythonSQLTypeScriptAutomated TestingModular ArchitectureData PipelinesExperimental Design
Soft Skills
Strong Product JudgmentIndependent ExecutionCollaborationTechnical CommunicationMentoring
Tools & Technologies
CI/CD PipelinesContainerizationLoggingMonitoringDebuggingAgent FrameworksLangChainADK
Industry Keywords
Advertising TechnologyMarketing AnalyticsSearchInformation RetrievalRecommendation Systems
Tech Stack
Tools & technologiesCloudPythonSQLTypeScript
About the role
Key responsibilities & impact- Translate business and product needs into well-defined problems, research plans, and technical designs
- Own AI capabilities end to end, from initial exploration through production deployment and ongoing improvement
- Develop and improve core algorithms
- Build production-grade AI agents for performance marketing, market research, and auto-ML
- Turn research into maintainable software, including reusable modules, data pipelines, and services with clear interfaces
- Implement automated tests and robust deployment practices
- Monitor system behavior and investigate failure cases in production
- Continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow
- Choose appropriate approaches across statistical methods, classical machine learning, and LLM-based systems
- Balance research depth, delivery speed, and operational complexity
- Partner with product and engineering to shape priorities and lead technical initiatives
- Review designs and code
- Mentor teammates
Requirements
What you’ll need- MSc or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field
- 5+ years of experience in data science or applied machine learning
- 2+ years in ML engineering or software engineering, with direct responsibility for deploying and maintaining production systems
- Proven ownership of significant AI products or features, taking ambiguous problems from initial concept to a working product used by real customers
- Strong foundations in machine learning and statistics, including experimental design and model evaluation
- Practical experience with NLP, embeddings, clustering, or related methods for analyzing unstructured data
- Strong Python, SQL, and TypeScript skills
- Solid software engineering practices, including 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
- Ability to define milestones, prioritize experiments, communicate technical trade-offs, and collaborate across product, engineering, and business teams
- Experience as a core technical contributor at a high-growth startup
- 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 requiring traceability, explainability, approval workflows, and reliable execution
Benefits
Comp & perks- Opportunity to build and shape a new category at the intersection of AI, marketing, and commerce
- Work with repeat entrepreneurs, world-class engineers, and marketing leaders
- Opportunity to work on tools used by Fortune 500 marketing teams