Compass

User Researcher

• Plan and conduct research for digital products
• Use qualitative and quantitative methods to understand user needs
• Identify usability issues
• Guide product decisions
• Consolidate findings and turn them into recommendations for Design, Product, and Technology
• Present results and support the prioritization of improvements

Mid-Level UX Researcher

AI Engineer

• Develop end-to-end Artificial Intelligence and automation solutions
• Take a hands-on approach, from understanding business needs through designing, developing, and deploying solutions
• Identify opportunities to apply Generative AI and automation to corporate processes
• Develop AI agents, chatbots, copilots, and RAG-based solutions
• Build intelligent workflows and automations by integrating different systems, APIs, and platforms
• Work with Generative AI architectures, LLMs, and LLMOps, pursuing robust, secure, and scalable solutions
• Engage with technical and business stakeholders, translating needs into technical solutions

Senior AI Engineer – Hybrid

SDET

• Lead the team’s test automation strategy by defining technical standards, architecture, and quality governance
• Design, structure, and evolve modular, robust, and maintainable automation frameworks
• Automate functional and non-functional tests across UI (Frontend) and API (Backend) layers
• Integrate and maintain automated test suites within CI/CD pipelines (Jenkins / GitHub Actions)
• Implement and promote a Shift-Left culture, actively contributing from the refinement and ideation stages alongside Product, Design, and Engineering
• Develop BDD-based test scenarios (Cucumber/Gherkin) as living documentation
• Execute and analyze performance and load tests (JMeter / K6), supporting the identification of bottlenecks
• Monitor and ensure the code quality of automation projects using tools such as SonarQube
• Manage test data sets in relational (MySQL) and non-relational (MongoDB) databases
• Manage test cases and test plans with traceability through Jira and Xray
• Mentor and support the technical development of Junior- and Mid-level QAs
• Translate technical risks and bug impacts into clear language for stakeholders and leadership

Senior Java QA Test Automation Engineer

Business Intelligence Developer

• Transform data from multiple sources into high-performance executive, tactical, and operational dashboards
• Support IT Governance coordination in analytics initiatives and the development of a data-driven business culture
• Lead the structuring of data sources
• Define KPIs and metrics aligned with the strategic vision
• Develop high-performance executive, tactical, and operational dashboards
• Connect and structure data from multiple sources, including APIs, SQL Server, and Data Lakes
• Ensure UX/UI best practices in data visualization
• Implement BI governance, security, and documentation standards

Senior Power BI Developer

Data Scientist

• Work on the modeling and simulation of Operations Research problems.
• Develop mathematical models for optimization and simulation problems.
• Implement computational models and solve them using appropriate solvers.
• Develop technology solutions that support Operations Research initiatives.
• Contribute to the development and evolution of applications using technologies such as Python and Java.
• Apply DevSecOps practices and concepts in solution development.

Data Scientist – Operations Research, Mid/Senior

Data Specialist

• Define and implement data solutions applied to industrial processes in the Energy industry
• Support initiatives related to Oil Exploration and Production, Refining, Gas Processing, and Thermoelectric Power Generation
• Apply knowledge of industrial physical and chemical processes to interpret and process data from sensors and industrial systems
• Contribute to solutions focused on production optimization, energy efficiency, operational and process safety
• Support initiatives involving Asset Performance Management (APM), operational and maintenance risk analysis, and operational excellence
• Collaborate with Data, BI, Data Engineering, and other specialists, incorporating industrial business knowledge into solutions
• Work with industrial data solutions and technologies such as AVEVA/PI, Cognite, and Dataiku, according to project requirements

Industrial Data Specialist – Oil & Gas

Solutions Engineer

• Participate in preparing technical feasibility studies, assessing alternatives, costs, risks, impacts, and benefits
• Define and evaluate solution architectures for strategic projects and initiatives
• Analyze technical alternatives, trade-offs, risks, and architectural impacts
• Support legacy system modernization initiatives and architecture evolution
• Define and evaluate integrations, APIs, microservices, and distributed architectures
• Work on initiatives involving Cloud, Security, Data, Observability, and Artificial Intelligence
• Support solution governance and technical assessments
• Develop architectures and documentation using the C4 Model
• Record architectural decisions through ADRs
• Apply and evaluate architecture and software Design Patterns when defining solutions
• Support POCs and evaluations of new technologies
• Monitor projects throughout implementation, assessing significant changes and alignment with architectural decisions
• Contribute to the evolution of standards

Solution Architect

• Design, develop, integrate, and deploy Generative AI, LLM, and intelligent agent solutions
• Transform business challenges and needs into automated, intelligent, scalable, and integrated solutions
• Develop workflows and automations using tools such as n8n, Microsoft Power Automate, and Microsoft Copilot Studio
• Build applications and services using Python, APIs, and integrations with enterprise systems
• Design and implement architectures based on LLMs, RAG, knowledge bases, chatbots, and AI agents
• Develop solutions using frameworks such as LangChain, LangFlow, n8n, or similar technologies
• Integrate AI solutions with cloud platforms and services, including AWS Bedrock/AgentCore, Azure OpenAI, and Vertex AI
• Apply LLMOps practices, including evaluation, monitoring, observability, security, governance, and continuous improvement of applications
• Develop and integrate the APIs, services, and components required to deliver AI solutions in production environments
• Participate in the entire solution development lifecycle, from understanding the problem and defining the architecture through deployment and production support
• Evaluate new AI technologies, frameworks, and approaches that can drive efficiency, automation, and innovation for the business
• Partner with business, technology, data, and other stakeholder teams to identify opportunities and define solutions
• Ensure best practices in software engineering, security, scalability, quality, and maintainability across developed solutions

AI Engineer – Mid-Senior

• Develop predictive models and Machine Learning solutions for complex business problems, from data exploration through model validation, interpretation, and deployment
• Translate business needs into analytical questions, technical requirements, datasets, and Data and Machine Learning solutions
• Develop Python solutions using supervised learning, unsupervised learning, and Deep Learning techniques
• Participate in the design and technical definition of new Data, Machine Learning, and Artificial Intelligence solutions and features
• Develop, enhance, and optimize data pipelines and platforms for large-scale processing using Spark/PySpark, Kafka, and/or Amazon Kinesis
• Work with AWS Data, Analytics, and Machine Learning services such as SageMaker, Glue, EMR, Athena, Redshift, and Lake Formation
• Implement, optimize, monitor, and support models, pipelines, and production features, ensuring performance, scalability, stability, and reliability
• Explore data and model results, identifying patterns, opportunities, and improvements for the business
• Apply best practices for experimentation, testing, version control, and lifecycle management of models and pipelines
• Collaborate with Engineering, Data, Product, and Business teams to build end-to-end solutions
• Solve problems and manage technical dependencies in dynamic, highly complex environments

Senior Data Scientist, Machine Learning, AI

Security Engineer

• Define and evolve the corporate Secure Software Development Life Cycle (SSDLC) process
• Establish minimum security requirements for applications
• Define and implement Security Gates in development and CI/CD processes
• Integrate security controls and requirements into the application lifecycle
• Support the advancement of a secure development culture across the organization
• Participate in projects from the initial conception and architecture stages
• Conduct security assessments of proposed solutions and architectures
• Identify risks and vulnerabilities and propose mitigation measures
• Apply Threat Modeling and Security by Design practices
• Work closely with architects, developers, DevOps, and other technical teams
• Identify, analyze, and remediate application vulnerabilities
• Support development teams in adopting Secure Coding practices
• Conduct code reviews from a security perspective
• Assess APIs, microservices, containers, and software components
• Address vulnerabilities and risks related to dependencies, code, infrastructure, and configurations
• Integrate security tools into CI/CD pipelines
• Work with SAST, DAST, SCA, Secret Scanning, IaC Scanning, and Container Security solutions
• Support the implementation and management of SBOMs
• Define criteria and policies for Security Gates
• Promote automation of security controls throughout pipelines
• Work in AWS environments, supporting the implementation of secure architectures
• Assess cloud security configurations and controls
• Work with resources such as AWS API Gateway and AWS Security Hub
• Support the identification and remediation of risks related to cloud infrastructure and applications

Senior Security Engineering Analyst – AppSec

• Develop predictive models and machine learning algorithms to solve complex business problems
• Conduct data exploration, analysis, validation, and interpretation with a focus on measurable impact
• Extract and transform data from relational databases using SQL
• Develop analytical solutions, automations, and data manipulation processes using Python
• Apply statistical modeling and machine learning techniques to data analysis
• Use machine learning, deep learning, and data visualization libraries and frameworks to build analytical solutions
• Develop solutions involving NLP, Generative AI, LLMs, and RAG (Retrieval-Augmented Generation)
• Serve as an interface between business and IT
• Present results and insights to non-technical audiences
• Support data-driven decision-making in pursuit of business objectives

Senior Data Scientist

• Design, develop, and deploy artificial intelligence solutions at scale
• Create conversational flows and AI agents
• Develop solutions using LLMs, LLMOps, and generative AI architectures
• Deliver robust, secure applications integrated with the organization’s technology ecosystem
• Create chains, agents, and memory flows using LangChain and LangGraph
• Deploy and manage the machine learning model lifecycle using AWS SageMaker
• Implement vector search and RAG architectures using AWS OpenSearch
• Manage metadata and history storage in a NoSQL database using AWS DocumentDB
• Develop and integrate web APIs using FastAPI or Flask
• Perform code version control using Git
• Apply Prompt Engineering practices
• Work with frameworks such as LangChain, LangFlow, n8n, or similar tools
• Work with cloud platforms such as AWS Bedrock (Agentcore), Vertex AI, and Azure OpenAI
• Apply LLMOps practices and API-based integration to build intelligent agents
• Support data-driven decision-making aligned with the client’s business objectives

Senior AI Engineer

Product Manager

• Manage and plan the product backlog, defining priorities based on business objectives and customer needs, taking quarter planning into account
• Create and write initiatives, epics, and user stories by gathering, documenting, and detailing functional requirements and business rules related to insurance, including auto insurance
• Lead refinement sessions with the technical team
• Provide partial delivery status updates for initiatives
• Coordinate with external partners
• Work on API integrations and insurers’ digital ecosystems
• Support the development of new products and initiatives related to digital services, including solutions focused on mobility, experiences, and financial services

Senior Product Owner

Product Analyst

• Support the Product Owner in managing, refining, and prioritizing the product backlog.
• Gather, organize, and document functional and non-functional requirements.
• Participate in Agile ceremonies, such as Planning, Refinement, Daily Stand-ups, Reviews, and Retrospectives.
• Track feature development in collaboration with Engineering, QA, UX/UI, and Data teams.
• Support the validation and acceptance testing of deliverables before they are released to production.
• Ensure that requirements are well-detailed, prioritized, and aligned with product objectives.
• Facilitate communication among business, technology, and other stakeholders.
• Gather feedback from users, internal customers, and partner teams to support continuous product improvement.
• Support the monitoring of metrics, incidents, bugs, and improvement opportunities.
• Contribute to the documentation of processes, workflows, business rules, and features.
• Support communication about releases, enhancements, and new features.
• Prepare reports and presentations on product progress, results achieved, and areas requiring attention.

Senior Product Analyst

Data Analyst

• Develop, publish, and maintain dashboards and reports in Power BI
• Create and optimize data models for analysis and visualization
• Develop queries and analytical routines using SQL
• Develop advanced measures and calculations with DAX
• Work with Data Lake architectures, organizing and consuming data from multiple sources
• Use cloud services for data storage and processing
• Ensure the quality, consistency, and reliability of information
• Translate business needs into clear analyses and metrics
• Support clinical and administrative teams with data-driven insights
• Work within agile methodologies, participating in Scrum ceremonies
• Apply FinOps concepts in the context of data and cloud environments