Mid-Level AI Analyst

Accountabilities

• Evaluate AI technologies and solutions: Assess artificial intelligence tools, including generative AI, based on technical feasibility, implementation costs, potential risks, and alignment with business objectives.

• Develop and document proofs of concept: Support the planning, execution, and documentation of AI proofs of concept (POCs), helping define success criteria, test hypotheses, and evaluate results.

• Support production implementation: Collaborate with technical teams to transition AI solutions from experimental environments into production, contributing to integration design, technical requirements gathering, and solution documentation.

• Promote responsible AI practices: Apply security, privacy, compliance, and responsible-use principles, considering applicable regulations such as Brazil's General Data Protection Law (LGPD) and internal organizational guidelines.

• Establish best practices and documentation: Contribute to the development of guidelines, technical documentation, and shared learnings related to AI development, implementation, and adoption.

• Collaborate across departments: Work closely with Technology, Information Security, Legal, Data, and Business teams to identify opportunities and facilitate the implementation of AI initiatives.

• Monitor and optimize AI solutions: Track the performance and outcomes of implemented solutions, identify areas for improvement, and recommend adjustments to enhance reliability, efficiency, and business impact.

• Support AI integration and architecture: Contribute to the evaluation of system architectures and API integrations to ensure AI solutions can operate effectively within existing technology environments.

Requirements:

• Educational background: Bachelor's degree in Computer Science, Engineering, Statistics, Information Systems, or a related field.

• Cloud computing experience: Practical experience with cloud platforms, particularly Amazon Web Services (AWS).

• AI implementation experience: Hands-on experience evaluating, testing, or implementing artificial intelligence solutions.

• Knowledge of AI technologies: Practical knowledge or experience with machine learning, large language models (LLMs), and generative AI applications.

• Proof-of-concept development: Experience participating in the planning, execution, testing, and evaluation of AI proofs of concept.

• Production deployment knowledge: Understanding of the processes and technical considerations involved in moving AI solutions from experimental stages into production environments.

• Systems architecture and integrations: Knowledge of system architecture and application integration through APIs.

• Data engineering fundamentals: Basic to intermediate knowledge of data engineering concepts and practices.

• AI governance and security: Familiarity with AI governance, information security, data privacy, and regulatory requirements, including LGPD.

• Analytical and problem-solving skills: Ability to assess technical alternatives, evaluate results, identify risks, and translate business needs into practical AI solutions.

• Communication and collaboration: Strong interpersonal skills and the ability to work effectively with technical and non-technical stakeholders across different departments.

Preferred qualifications:

• Postgraduate studies, specializations, or certifications in Artificial Intelligence, Data Science, or Cloud Computing.

• Familiarity with MLOps tools, model deployment pipelines, and practices for managing AI solutions throughout their lifecycle.

• Knowledge of prompt engineering and generative AI techniques, including Retrieval-Augmented Generation (RAG) and fine-tuning.

• Experience monitoring, evaluating, and optimizing AI solution performance.

• Familiarity with responsible AI frameworks, governance standards, and security best practices.

• Advanced English proficiency.

Benefits:

• Health and dental coverage: Access to healthcare benefits designed to support your physical well-being.

• Meal and food allowances: Financial support for everyday meal and grocery expenses.

• Childcare assistance: Support to help employees manage childcare costs.

• Extended parental leave: Extended paternity leave to support family life and caregiving responsibilities.

• Health and wellness partnerships: Access to gym networks and wellness professionals through programs such as Wellhub (Gympass) and TotalPass.

• Profit sharing: Participation in profit-sharing and results-based compensation programs (PLR), subject to applicable eligibility criteria.

• Life insurance: Coverage designed to provide additional financial protection.

• Continuous learning: Access to a dedicated learning platform, university-style training resources, and partnerships with online education providers.

• Language development: Access to language-learning platforms to support professional growth and communication skills.

• Employee discounts: Access to a discount club offering savings on selected products and services.

• Physical and mental well-being: Free online resources dedicated to physical health, mental wellness, and overall quality of life.

• Family support and education: Access to pregnancy and responsible-parenting courses.

• Inclusive and accessible workplace: Support from dedicated health and well-being teams, inclusion specialists, and employee affinity groups throughout the recruitment and employment journey.

• Remote work: A remote position based in Brazil, offering the flexibility to work outside a traditional office environment.