Brazil
Pentera
XP Inc.
Machine Learning Engineering - MLOps | Data Science Specialist
Brazil
São Paulo, São Paulo, Brazil
PythonSQLDockerKubernetesTerraform
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About the opportunity
About Us
XP Inc. is one of the largest independent financial institutions in Brazil, owning brands such as XP, Rico, Clear, XP Educação, and InfoMoney, among others. With over 4,6 million active clients and assets under custody exceeding R$ 1,1 trillion, it has been transforming the financial market for 25 years to improve people's lives.
With a strong culture guided by four values - Dream Big, Entrepreneurial Spirit, Customer Focus, and Open Mind - XP Inc. is always seeking the best talents with the ambition to achieve the impossible.
🚀 About the Opportunity
The Data Science area's mission is to apply machine learning techniques to generate scalable solutions, monitor model performance, and automate processes, supporting the organization's decision-making and results optimization.
We are looking for an MLOps/ML Engineering Specialist to be primarily responsible for the area's MLOps pipeline and the guardian of model quality in production. This is a technically oriented and technical mentorship position, where you will act as a multiplier: defining standards, unblocking bottlenecks, and ensuring the Data Science area scales with quality and governance.
⏳ Process Stages
Learn more about our day-to-day on Instagram and LinkedIn.
Check out our reviews on Glassdoor.
- Initial screening: Resume analysis to verify suitability for the qualifications and requirements of the position.
- General tests: Assessment of cultural alignment with the Mindsight test and logical reasoning test from Predictive Index.
- Interview with the Recruitment team: Discussion about expectations regarding the position and professional goals.
- Technical assessment: Interview focused on the specific skills required for the role.
- Interview with leadership: A conversation to discuss your vision and how you can contribute to the team and the company.
- Interview with colleagues: Interaction with future peers to assess synergy and teamwork.
- Culture-focused interview: Exploration of the company's values and principles to ensure a good cultural fit.
- Offer: Presentation of the job proposal, including details on compensation and benefits.
Flexible In-Office Work Model
Our work model varies by role, potentially being fully in-office for business fronts and more flexible for other teams.
We follow a model with more frequent in-office presence, but always guided by flexibility and autonomy, with the responsibility of our entrepreneurial culture.
Here at XP Inc., we value personal interactions and believe in using the office as a tool to enhance our work relationships.
*By proceeding with the application, you declare that you have read, understood, and agree to the Privacy Policy of Grupo XP Inc. and its suppliers and partners supporting the selection process. Grupo XP Inc. may use artificial intelligence solutions to support the recruitment team during the process, which may involve the processing of your personal data for this purpose, in accordance with the principles of the General Data Protection Law and the Privacy Policy of Grupo XP Inc.
🔍 What We're Looking For
Essential Prerequisites
- Bachelor's or Master's degree in Computer Science, Data Science, or related fields.
- Proven experience with MLOps and Machine Learning Engineering.
- Proven experience in building MLOps pipelines, not just consuming ready-made tools.
- Solid experience with Databricks (Spark, Delta Lake, MLflow, Unity Catalog, Feature Store).
- Programming in Python and PySpark (SQL as a complement).
- Experience with data and ML pipelines, including model standardization and experimentation (Airflow, MLflow, etc.).
- Experience with feature stores.
- Experience with model explainability techniques and tools (SHAP, LIME, etc.).
- Experience with statistical modeling (inference, hypothesis testing, regression, time series, probabilistic modeling).
- Experience with CI/CD tools (GitHub Actions, GitLab CI, etc.).
- Hands-on experience with Azure.
- Knowledge of Docker and Kubernetes (including AKS).
- Alignment with our culture.
- Reside in São Paulo and have availability for a hybrid regime (3 days in the office per week).
Differentiators
- Generative AI and agentic solutions (LLMs, RAG, fine-tuning, embeddings, prompt engineering, multi-agent orchestration, tool calling, memory), as a future vision for the area.
- Azure AI Foundry for development, deployment, evaluation, and governance of GenAI and agent applications.
- Observability/evaluation frameworks for LLMs and agents (LangSmith, Ragas, Arize).
- Market feature stores (Feast, Tecton) and best practices to avoid training-serving skew.
- IaC / automation (Terraform, Bicep).
- Model standardization and experimentation with additional tools (Kedro, DVC, Metaflow).
- Data streaming (Kafka, Event Hubs).
- GPUs in Kubernetes, quantization, and inference optimization for LLMs (e.g., KAITO/AKS).
- Data governance and model security.
🎯 Challenges and Impact
- Be the primary responsible for the MLOps pipeline, leading its end-to-end construction, evolution, and operation.
- Ensure the quality of models in production, acting as the guardian of Machine Learning standards in production (reliability, testing, documentation, and SLAs).
- Design and maintain data and ML pipelines, from training to deployment and production monitoring, in a modular, versioned, and reproducible manner.
- Enable the Data Science team to adapt to the pipeline standard, disseminating ML engineering best practices.
- Technically mentor professionals in training through design reviews, pairing on complex problems, and elevating the team's technical level.
- Operate the ML infrastructure in the cloud, optimizing the performance, cost, and reliability of model inference and serving.
- Act as a bridge between technical areas (Engineering, Data Engineering, Governance, DevOps, Access Management, and Architecture), anticipating dependencies, risks, and blockers impacting Data Science.
- Collaborate with Data Science, Data Engineering, Product, and Business on prioritizing, designing, and delivering solutions.
Benefits
Health and well-being
- Health plan
- Dental plan
- Wellhub (Gympass)
- Zenklub
- Life Insurance
- iFood Benefits (flexible meal and food vouchers)
- Transportation Voucher
- New Value (benefits club)
- Parental leave: maternity for 6 months and paternity for 20 days.
- Childcare Assistance
Financial Life
- Exclusive Investment Funds
- Investment Advisory
- XP Visa Infinite Card with no annual fee
- Credit (payroll loans, home equity, real estate CCB, etc.)
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