Machine Learning Engineer

Docket

Machine Learning Engineer developing Intelligent Document Processing solutions for Docket. Collaborating on complex data extraction and predictive analytics projects.

Posted 7/22/2026full-timeRemote • 🇧🇷 BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and optimizing Intelligent Document Processing solutions and Agentic AI workflows, with strong capabilities in Python, SQL, and advanced statistical modeling. Proven ability to mentor junior team members and communicate complex technical concepts effectively to diverse stakeholders.

Highest-signal resume keywords

Intelligent Document Processing (IDP)Agentic AI Development (LangGraph)Machine Learning / Generative AI ModelsAdvanced Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow)Evaluation Frameworks (Evals)

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonSQLAdvanced StatisticsMachine LearningGenerative AIData ManipulationStatistical ModelingDocument Processing (OCR/NLP/CV)API DevelopmentMicroservices

Soft Skills

CommunicationMentorshipCollaborationProblem-SolvingTechnical Leadership

Tools & Technologies

LangGraphN8nGoogle Document AIAWS TextractAzure Form RecognizerTesseractLayoutLMRagasTruLensDeepEval

Industry Keywords

Data ScienceComputer VisionNatural Language ProcessingStatistical AnalysisProduction Environment

Tech Stack

Tools & technologies

AWSAzureNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Lead and architect IDP (Intelligent Document Processing) solutions: develop, train, and optimize robust pipelines for extracting unstructured data from complex documents, defining efficient fine-tuning, RAG, and computer vision strategies.
Architect Agentic AI development: design and productionize complex agentic workflows using LangGraph, n8n, and others, ensuring state management, resilience, low latency, and failure handling in the orchestration of the formalization and credit pipeline.
Model structured data for new AI products: build statistical pipelines and advanced analytical models on the data extracted by IDPs, transforming raw data into strategic business reports, market indices, and predictive intelligence.
Ensure Quality, Observability, and Cost (Evals & FinOps): structure automated evaluation frameworks (LLM Evals) to measure hallucination, accuracy, and recall of models in production, optimizing token consumption and infrastructure costs.
Autonomy and cross-functional collaboration: work directly with Product Managers, Engineering, and Technical Leadership to translate market pain points into viable architectures, participating actively from PoC design through final production delivery.
Mentorship and technical culture: support the technical development of more junior team members, disseminating best practices for coding, modeling, and AI usage.

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, Physics, Data Science, or a related quantitative field.
Proven hands-on experience: a consistent track record of developing and maintaining Machine Learning / Generative AI models in production environments.
Strong Python and SQL skills: write clean, modular, testable, and scalable code for advanced data manipulation, and for consuming and building APIs.
Advanced statistics and modeling: solid knowledge in descriptive and inferential statistics, multivariate analysis, and hypothesis testing applied to data product development.
Production mindset and autonomy: ability to take complex, poorly structured problems and transform them into end-to-end functional solutions.
Communication and mentorship: ability to explain complex technical concepts to non-technical stakeholders and mentor junior team members.
Advanced Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow) and advanced SQL (query optimization, dimensional modeling, and joining large volumes).
IDP and Generative AI in production: practical experience with fine-tuning LLMs/SLMs, Agentic AI frameworks (LangGraph required or highly desirable, LangChain, AutoGen), complex RAG architectures, and advanced prompt engineering.
Evaluation frameworks (Evals) and observability: experience using LLM/IDP testing and evaluation frameworks (e.g., Ragas, TruLens, DeepEval, LangSmith, or proprietary solutions).
Document Processing (OCR/NLP/CV): proficiency with leading market tools and libraries for text extraction and computer vision (Google Document AI, AWS Textract, Azure Form Recognizer, Tesseract, LayoutLM, etc.).
Architecture and best practices: solid understanding of software architecture, consuming/creating REST APIs, microservices, and code versioning (Git).

Benefits

Comp & perks

Meal and food allowance via Flash for when hunger strikes.
Health and dental insurance to take good care of you.
Life insurance to provide peace of mind.
Petlove, because at Docket we understand your furry family matters too.
Conexa Saúde, psychological support at your fingertips.
Wellhub and TotalPass to keep your body moving.
Galena, because learning and development are essential!
Partnership with Sesc for leisure and cultural activities.
Childcare allowance for parents with children up to 5 years old.
Baby Cash when the family grows.
Day off during your birthday month to celebrate as you deserve.
Schedule: 44-hour workweek.