Pessoa AI Engineer Analista III - Vaga Afirmativa para Pessoas com Deficiência (PcD)

Accountabilities: Develop, integrate, and maintain AI-powered solutions that support order processing and operational workflows within the OMS ecosystem. Design, test, and optimize prompts to improve the accuracy, reliability, and usefulness of LLM-powered applications. Build and maintain RAG pipelines that enable accurate retrieval and use of structured and unstructured data. Integrate AI models and conversational workflows with internal APIs, relational databases, and external retail and logistics tools. Support LLMOps activities by monitoring model performance in production, tracking hallucinations and latency, and observing token consumption and associated costs. Collaborate with product specialists and cross-functional teams to translate OMS business rules into simple, effective AI-driven and agent-based solutions. Rapidly prototype and implement solutions that address immediate operational needs and reduce manual tasks and analysis time for operations teams. Contribute to the ongoing improvement and progressive redesign of the product architecture. Identify opportunities to accelerate delivery, improve workflows, and increase the overall efficiency of the product and its users. Requirements: Strong Python programming skills, including data structures, object-oriented programming, and the development and consumption of REST APIs. Practical experience integrating LLM APIs, such as OpenAI, Gemini, Anthropic, or similar platforms. Familiarity with AI development frameworks and tools such as LangChain, LlamaIndex, or LangGraph. Proficiency in SQL and experience working with data formats such as JSON. Basic knowledge of at least one cloud provider, such as AWS, Google Cloud Platform (GCP), or Microsoft Azure. Experience using Git or other code versioning workflows. A hands-on, problem-solving mindset, with the ability to understand user needs, explore business impacts, and rapidly prototype practical solutions. Ability to collaborate effectively with multidisciplinary teams and translate business requirements into technical implementations. Nice to have: A hybrid background combining Data Science or Machine Learning expertise with practical AI Engineering experience. Familiarity with the Model Context Protocol (MCP) or advanced context-management approaches for LLMs, such as SDD. Understanding of enterprise agentic solutions, LLM orchestration, and complex reasoning workflows involving multiple tools. Benefits: Health and well-being: Flexible medical and dental plans, medication assistance, health support for family members, free psychotherapy sessions, 24/7 telemedicine, preventive care programs, mental health initiatives, and vaccination programs. Food and nutrition: Meal vouchers or access to a local restaurant, food allowance, and an additional food allowance during the holiday season. Remote work support: Home office allowance for eligible remote and hybrid arrangements. Fitness and lifestyle: Access to Gympass/TotalPass for gyms and fitness studios, plus a birthday day off. Product discounts: Discounts of up to 40% on products from the company's brand ecosystem. Pet care and travel: Pet health plan options, partner discounts, and travel and accommodation programs. Family support: Extended parental leave of up to 180 days for mothers and 120 days for fathers, support programs for parents of children with disabilities or additional needs, childcare assistance, and early childhood education support. Education and family assistance: School supply assistance, child nutrition credit, and dedicated legal, psychological, and social guidance. Financial security: Private pension plan, life insurance, bereavement assistance for the loss of parents, and payroll-deductible loan options. Transportation: Transportation vouchers and parking benefits, depending on the work arrangement and eligibility. Work arrangement: Fully remote position. Additional details: Eligibility for individual benefits may vary according to the position and work arrangement. Salary and compensation details were not specified in the original job description and can be confirmed during the recruitment process.