Brazil
Bunge
Experian
Staff AI Engineer
Brazil
São Paulo, São Paulo, Brazil
PythonSQLAWS
Automatic translation. Check the original.
Company Description
Serasa Experian is the first and largest Datatech in Brazil. We are a leader in intelligence solutions for risk and opportunity analysis, focusing on credit journeys, authentication, and fraud prevention. With cutting-edge technology, innovation, and the best talent, we transform the uncertainty of risk into the best decision, helping people achieve their dreams and companies of all sizes and segments to prosper.
We have 22.000 people operating in 32 countries, and every day we are investing in new technologies, talented professionals, and innovation to help all our clients maximize every opportunity. Headquartered in Dublin, Ireland, Experian is listed on the London Stock Exchange (EXPN) and is a constituent of the FTSE 100 index.
Job Description
We are looking for a Staff AI Engineer to lead the strategy for the use and adoption of Artificial Intelligence in our Marketing Services products, connecting technical depth in GenAI with concrete product opportunities, operational efficiency, and value generation for the Business Unit.
This is a senior individual contributor position: we expect you to build, decide, and be accountable for what you put into production. Additionally, you will functionally lead other AI Engineers—providing technical direction, reviewing architecture, establishing standards, and developing the team—without formal hierarchical management at this time.
The position is central to the evolution of solutions based on LLMs, RAG, and agents, and is responsible for identifying use cases, defining technical approaches, and transforming experimental learning into direction for Product, Engineering, and Leadership.
More than just a reference in experimentation, we are looking for someone who has already applied AI to accelerate digital product strategies, improve journeys, and sustain business decisions with real, measured impact.
Qualifications
Key Responsibilities
- Conduct comparative experiments with different LLMs, agents, and GenAI architectures applied to real business cases;
- Design, optimize, and evolve RAG pipelines, including strategies for chunking, embeddings, retrieval, and continuous evaluation;
- Structure evaluation and benchmarking frameworks for GenAI applications, considering quality, relevance, reliability, cost, latency, and adherence to the use case;
- Measure performance, cost, and ROI of AI-based solutions, supporting decisions on prioritization and scaling;
- Decide when to use Prompt Engineering, RAG, Fine-Tuning, or multi-agent architectures, taking responsibility for the technical and business trade-offs of the choice;
- Partner with Product, Engineering, Data, and Leadership teams to identify where AI can accelerate digital product strategies and increase value for the Business Unit;
- Translate technical results into clear recommendations for roadmap, architecture, governance, and capability evolution;
- Establish best practices, technical standards, and responsible use of GenAI within the structure, with actual adoption by the squads;
- Build and lead the applied AI vision for Marketing Services, connecting technological trends to the real needs of clients, partners, and products;
- Raise the technical level of the team in GenAI through mentoring, architecture reviews, and documentation of decisions.
Minimum requirements
Mandatory Requirements
- 5+ years of experience in software engineering, data, or Machine Learning, with a track record of increasing technical responsibility;
- Have led technological transformation at the area, Business Unit, or organizational scale, being able to describe the previous state, what changed, and the measured result. Examples: adoption of AI or GenAI in products and teams, platform migration, MLOps program, architectural change, or evolution of the engineering model;
- Have established standards, components, or technical practices adopted by teams other than your own, including how the adoption was achieved;
- Proven experience taking GenAI solutions to production—RAG and/or agents with real users, maintenance, and measurement of quality and cost. Proofs of concept, notebooks, or pilots that did not reach users do not count;
- Practical experience with GenAI and LLMs in a real business context;
- Experience in the design, evaluation, and evolution of solutions with RAG, embeddings, retrieval, and/or agents;
- Experience in initiatives that connected AI to digital products, operational efficiency, automation, or journey acceleration;
- Advanced proficiency in Python;
- Solid knowledge of SQL and handling large volumes of data;
- Consistent foundation in statistics, data analysis, and experimentation;
- Experience with the evaluation of generative models, including traditional metrics and LLM-based approaches;
- Familiarity with LLMs frameworks and ecosystems, such as OpenAI SDK, LangChain, CrewAI, or equivalents;
- Experience building MCP (Model Context Protocol) servers—designing tools, exposure contracts, and operation;
- Ability to document trade-offs and transform technical conclusions into actionable recommendations.
- Have developed engineers with observable evolution: mentoring, ramping up new people on the topic, raising the technical level of the team;
- Have influenced roadmaps and architectural decisions without formal authority, in contexts with divergent interests;
- Have led a contested technical decision through to adoption—and know what was necessary to convince others, or what caused you to change your position.
Languages
- Advanced English (mandatory)—reading, writing, and speaking for participation in forums, committees, and global company initiatives.
- Fluent English (preferred).
Preferred Qualifications
- Experience in accelerating AI adoption within a Business Unit, connecting technology to strategy and execution;
- Experience with digital products, personalization, automation, Martech, or growth-oriented journeys;
- Experience with Fine-Tuning, Transfer Learning, Hugging Face, PyTorch, or TensorFlow;
- Familiarity with AWS Bedrock Agents / AWS Agent Core;
- Experience with automation and orchestration of GenAI flows, using n8n, Flowise, LangFlow, or equivalents;
- Experience with Prompt Optimization, including DSPy or similar approaches;
- Experience in team coordination or formal people management—the structure is expected to grow, and team leadership is a possible evolution path;
- Ability to navigate well between technical exploration and executive prioritization.
Additional Information
Expected Competencies
- Decides with evidence. Supports technical choices with data, experiments, and measurements—not with preference or authority;
- Transforms ambiguity into direction. Receives a poorly defined problem and returns a path with scope, risk, and success criteria;
- Communicates in both registers. Explains the same decision to an engineer and an executive without losing precision or filling it with jargon;
- Takes ownership of the result, not just the delivery. Monitors what was built in production—quality, cost, incidents—and corrects it;
- Separates hype from value. Recognizes when AI is not the answer and says so, even when the expectation is the opposite.
Expected Scope
- Relevant influence on architectural decisions and GenAI strategy, spanning multiple Marketing Services squads;
- Technical reference for the Business Unit squads: standards, architecture reviews, and raising the team's technical level;
- Functional leadership of other AI Engineers, with technical direction and team development;
- Active and measurable role in accelerating the AI adoption curve in Marketing Services;
- Connection between technical excellence and product and business direction.
How we will measure success
- GenAI use cases in production, built within the structure;
- Reduction in lead time between idea and production;
- Inference cost known, instrumented, and optimized per use case;
- Components and standards published and effectively adopted by the squads;
- Business Unit autonomy in AI: less dependence on external queues to evolve products.
What should differentiate this profile
We are looking for someone who combines technical excellence in GenAI with an applied business vision, and who has already transformed technical capability into real organizational change—not just delivered projects. The right person here reads the system, anticipates priorities, leads relevant changes without formal authority, and moves the structure through technical direction and scalable standards.
Additional Information
Serasa Experian is much more than you imagine. With the purpose of creating a better future by expanding opportunities for people and companies, in Brazil we are more than 4 thousand people working in diverse teams and specialties. Here, every knowledge and diversity complements each other, and you can work on what you love most. We are committed to building an inclusive culture and an environment where people can balance their careers with their personal commitments and interests, valuing well-being.
We dedicate ourselves to being one of the best and most innovative companies to work for in the country, enabling incredible experiences and careers for our people. Our strong people-first approach is recognized externally through various market certifications: we have been awarded by Great Place To Work™ in 24 countries and by the international Top Employers certification, in addition to being recognized as one of the best companies for young professionals and having a rating of 4,6 on Glassdoor. Each recognition indicates that we are on the right path, providing an increasingly better work environment for our talent.
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Contract Type: Regular
Work Model: Hybrid
Department: Technology
Work Period: Full Time
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