Interested in this AI/ML Engineer role at Accenture Brasil?
Apply Now →About This Role
A Accenture ajuda as principais empresas do mundo a se reinventar, construindo seu núcleo digital e liberando o poder da IA para gerar valor rapidamente para organizações de todos os setores. Nossa estratégia é ser a parceira de reinvenção preferida de nossos clientes, liderar a adoção segura e ampla da IA e ser a empresa mais focada no cliente, habilitada por IA e um excelente lugar para trabalhar em todo o mundo.
Reunimos o talento de aproximadamente 814 mil pessoas com ativos e plataformas proprietários, profundo conhecimento de processos e setores e relacionamentos líderes no ecossistema para oferecer soluções de ponta a ponta e resultados mensuráveis em escala. Por meio de nossos Serviços de Reinvenção, oferecemos ampla experiência em Cibersegurança, Núcleo Digital, Finanças, Indústria e Empresa, Song, Cadeia de Suprimentos e Engenharia e Talentos, com capacidades avançadas em IA e Dados, Indústria e Processos e Tecnologia. Atendemos aproximadamente 9 mil clientes e geramos cerca de US$ 74 bilhões em receita no ano fiscal de 2026.
Visite a Accenture em accenture.com.
O que você vai fazer no seu dia a dia:
- Manter e aprimorar o programa global de Compliance de Dados e Inteligência Artificial, alinhando-o à estrutura de compliance da Accenture e às principais práticas do mercado;
- Identificar requisitos jurídicos, regulatórios e de políticas, atuais ou futuros, transformando-os em políticas, padrões, controles e ações aplicáveis ao negócio;
- Desenvolver ferramentas, conceitos, avaliações e revisões de compliance, com atenção especial às atividades e iniciativas de maior risco;
- Trabalhar em parceria com as áreas de Riscos, Segurança da Informação, Jurídico, Responsible AI e demais áreas envolvidas na gestão do programa;
- Apoiar avaliações de clientes, análises contratuais e iniciativas relacionadas a novos serviços, ofertas, projetos e produtos;
- Representar a área em comitês e grupos de trabalho, além de produzir orientações, treinamentos e conteúdos sobre governança de dados, inteligência artificial e compliance;
- Liderar e colaborar com uma equipe global e diversa, construindo relações de confiança com stakeholders internos, externos, clientes, especialistas e órgãos reguladores.
O que estamos procurando na pessoa que vai fazer parte do time:
- Experiência mínima de seis anos em Compliance, Ética ou Governança, preferencialmente em uma organização multinacional ou em um escritório de advocacia de grande porte;
- Conhecimento aprofundado de governança e gestão de dados, inteligência artificial, estratégias, frameworks e requisitos legais e regulatórios relacionados;
- Experiência mínima de três anos com liderança de equipes, incluindo atuação com profissionais e stakeholders em diferentes localidades;
- Capacidade de desenvolver uma visão estratégica e transformá-la em programas, políticas, padrões, controles e ações práticas;
- Inglês fluente e excelente comunicação verbal, escrita e interpessoal, com capacidade de sintetizar análises jurídicas complexas e apresentar recomendações claras para diferentes públicos.
Além disso, é desejável conhecimento:
- Formação em Direito ou mestrado ou formação equivalente em Informática, Ciência de Dados, Gestão da Informação, Informática de Negócios ou áreas relacionadas;
- Certificações em Privacidade de Dados, Governança de Dados, Auditoria, Inteligência Artificial ou Segurança da Informação;
- Experiência em gestão de projetos e capacidade de conduzir diferentes atividades e prioridades simultaneamente;
- Vivência no acompanhamento de tendências tecnológicas, jurídicas e regulatórias e na conversão dessas mudanças em requisitos aplicáveis;
- Experiência na construção de relacionamentos profissionais com especialistas, clientes e órgãos reguladores.
Requisitos de trabalho:
- Disponibilidade para viagens, conforme a necessidade da função, com previsão inferior a 10%;
- Capacidade de trabalhar remotamente com equipes e stakeholders globais;
- É necessário anexar o currículo em inglês durante a candidatura.
Benefícios:
🏥 Assistência Médica – 100% subsidiada pela empresa (titular e dependentes);
🦷 Assistência Odontológica;
🍽️ Vale Refeição ou Alimentação sem desconto;
🛡️ Seguro de vida;
📈 Previdência Privada;
🏋️ Wellhub;
💪 TotalPass;
📉 Opção de compra de ações da empresa com desconto;
💊 Desconto Farmácia;
👶 Auxílio Creche;
📚 Parceria com escola de idiomas;
👨👩👧 Licença Maternidade e Paternidade Estendida;
💰 PPR;
- Conforme política vigente
Localidade da vaga:
São Paulo – São Paulo
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Accenture Brasil, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Accenture Brasil AI Hiring
Accenture Brasil has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in São Paulo, São Paulo, Brazil.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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