Marketing AI/ML Engineer
About the Role
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
Morningstar is an equal opportunity employer.
Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.
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