Senior Data Scientist - Generative AI
About this role
BEES requires a senior practitioner to address the challenge of scaling and grounding live generative AI and search systems with real product and brand data. Operating within the AB InBev Growth Group, the selected individual will lead the GenAI platform by managing prompt-driven solutions, diffusion-based creative pipelines, and retrieval-augmented generation architectures. Responsibilities include building production-grade services that process large-scale datasets while optimizing both retrieval quality and latency.
This position exists to guide technical direction for how the team evaluates and builds search and GenAI applications that serve live traffic. Candidates who succeed here possess strong hands-on experience with vector retrieval, LLM APIs, and cloud orchestration platforms like Azure and Databricks. The ideal professional thrives in an agile, cross-functional setting and enjoys mentoring peers on retrieval best practices.
What you will do
- Design and deploy end-to-end generative AI systems, incorporating prompt engineering and retrieval-augmented generation architectures to ensure brand-safe outputs.
- Manage the search and retrieval layer by working with embedding models, vector similarity search, and hybrid search techniques.
- Apply frontier methods such as fine-tuning, multi-step RAG, and multimodal generation to solve complex creative challenges.
- Process large datasets at scale using cloud and orchestration platforms like Azure, Databricks, and Spark.
- Develop production models with Python utilizing frameworks like FastAPI and PySpark.
- Build evaluation and automation workflows, including offline evaluations, A/B testing, and quality monitoring.
- Collaborate across functions with product managers, data engineers, and business stakeholders while mentoring other team members.
What you need
- Hold a bachelor's degree in computer science, engineering, mathematics, or a related quantitative field.
- Demonstrate relevant professional background in data science or applied machine learning with a focus on LLMs, multimodal generation, and RAG architectures.
- Bring practical experience in search and retrieval systems, covering vector search, hybrid search, re-ranking, and performance trade-offs.
- Show familiarity with LLM and embedding APIs from providers such as Azure OpenAI or OpenAI.
- Utilize cloud infrastructure including Azure and Databricks for big data processing and scalable system design.
- Exhibit proficiency in Python and PySpark alongside API frameworks like FastAPI for production services.
- Apply development best practices using version control tools like GitHub and testing tools such as pytest and mypy.
Nice to have
- Master's degree or PhD in a quantitative discipline
Benefits
- Performance based bonus
- Attendance Bonus
- Private pension plan
- Meal Allowance
- Health, dental, and life insurance
- WellHub partnership
Skills and tech stack
Questions candidates ask
What is the remote work policy?
The posting mentions a casual office and dress code in Campinas, São Paulo, Brazil, but does not state a remote work policy.
What compensation and bonuses are offered?
Specific salary figures are not disclosed, but the package includes a performance-based bonus and an attendance bonus.
What tools and technologies will I use daily?
Daily technical work involves Python, FastAPI, PySpark, Azure, Databricks, Spark, and various LLM and embedding APIs.
Who will I work with on the team?
You will collaborate closely with cross-functional teams including data engineers, machine learning and platform engineers, business stakeholders, and product managers.