We’re looking for a Marketing AI / Machine Learning Engineer to join the Analytics Engineering team within Marketing Intelligence and Operations (MIOps). This role focuses on building and operationalizing AI-driven systems that improve marketing measurement, automate workflows, and scale insight generation.
You will work across the full lifecycle of AI solutions, partnering with Marketing, Analytics, and Engineering to turn business problems into scalable, production-ready systems. These solutions may include machine learning models, generative AI workflows, and agent-based automation.
Role Scope & Impact
Build and scale AI-driven systems that support marketing measurement, experimentation, and decision-making
Develop automation and agent-based workflows that reduce manual analysis and operational overhead
Ensure outputs are interpretable, reliable, and aligned to business context
Contribute to a modern marketing intelligence ecosystem combining ML, GenAI, and analytics engineering
This role is not about owning a single model or tool. It is about helping Marketing move faster and smarter by embedding AI into how work actually gets done.
Responsibilities
Design, develop, and deploy machine learning and generative AI solutions for marketing use cases
Build and maintain scalable data and model pipelines across the ML lifecycle (data prep, modeling, evaluation, deployment, monitoring)
Develop GenAI capabilities including prompt workflows, embeddings, and retrieval-augmented generation (RAG) patterns
Contribute to AI agents and automation workflows that streamline marketing analysis and operations
Partner with Marketing and Analytics teams to translate business needs into technical solutions
Perform data preparation, feature engineering, and validation across marketing and enterprise data sources
Integrate AI outputs into dashboards, tools, and downstream workflows
Document systems, models, and outputs to ensure transparency and usability
Requirements
Bachelor’s degree required; Master’s preferred in a quantitative field
1–3 years of experience in ML, data science, analytics engineering, or software engineering
Strong foundation in machine learning (regression, classification, clustering, evaluation)
Proficiency in Python and SQL for data and model development
Experience with standard ML/data libraries (Pandas, NumPy, Scikit-learn)
Familiarity with GenAI concepts (prompting, embeddings, vector search, evaluation)
Exposure to modern data platforms (Snowflake, Databricks, BigQuery) and version control (Git)
Ability to work cross-functionally and communicate technical concepts clearly
Nice to Have
GenAI / LLMs
Experience with LLM frameworks (LangChain, LlamaIndex, Semantic Kernel)
Experience with RAG systems and vector databases
Familiarity with LLM APIs (OpenAI, Anthropic, Azure OpenAI, open-source)
Experience evaluating LLM outputs (quality, bias, hallucination)
AI Systems / Ops
Exposure to MLOps / LLMOps (experiment tracking, monitoring, CI/CD)
Experience with agent-based workflows or orchestration
Domain / Tools
Experience with marketing tech or analytics (CRM, paid media, web analytics)
Experience with BI tools or analytics workflows
Demonstrated side projects or experimentation in AI/ML