Senior AI Engineer – Tech Lead

Maxxi; São Paulo; On-site; BRL 280,000 - 420,000; Full time

Job summary

Maxxi seeks a Senior AI Engineer and Delivery Lead to advance our AI Transformation by building scalable Data Science and AI solutions that enhance Commercial and Regional processes. You will work as both an engineer and technical lead for a team of Data Scientists, Engineers, and Analysts on end-to-end ML pipelines.

You will own the ML lifecycle from data pipelines to deployment and monitoring, collaborating with cross-functional teams to translate business requirements into technical AI

Qualifications

• Degree in Computer Science, AI, ML, Data Science, Engineering, or related fields.
• 3–5 years hands-on experience in AI/ML engineering or related roles.
• Proven track record of deploying models to production environments.
• Proficiency in Python.
• Good understanding of AWS cloud architecture including SageMaker and Bedrock.
• Ability to identify and reuse GitHub projects to solve business problems.
• Experience working with cross-functional teams and translating business needs into technical solutions.
• Demonstrated ability to manage multiple projects and delivery results in agile environments.
• Fluent English, with the ability to communicate effectively with global teams.

Responsibilities

• Design, develop, and deploy production-ready AI/ML models and applications that solve critical business problems.
• Lead the AI Transformation in the region.
• Educate and evangelize Data Science and AI in the region.
• Drive engagement and adoption of Data Science and AI; shape the Data Science and AI strategy for the region.
• Own the complete ML lifecycle: data pipeline design, feature engineering, model training, evaluation, deployment, and monitoring.
• Collaborate with cross-functional teams to translate business requirements into technical solutions.
• Implement MLOps best practices including model versioning, CI/CD pipelines, and automated retraining.
• Translate complex business requirements into technical AI/ML solutions with clear success metrics.
• Conduct code reviews and establish engineering best practices for AI projects.
• Evaluate and integrate emerging AI technologies (LLMs, Generative AI, and RAG) into the client’s ecosystem.
• Lead POCs and MVPs using design thinking methodologies to validate solution feasibility.
• Optimize model performance, latency, and cost-efficiency for production systems.
• Contribute to the client’s AI platform capabilities and reusable component libraries.

Skills

Python

AI/ML engineering

AWS SageMaker

Bedrock

MLOps

GitHub

Cross-functional

English fluency

Education

Degree in Computer Science or related

Tools

GitHub

CI/CD

Job description

We are looking for a Senior AI Engineer and Delivery Lead to support our AI Transformation by identifying and developing scalable Data Science and AI solutions that enhance Commercial and Regional Processes, driving measurable business impact across all client areas.In this role, you will work both as an engineer, developing Data Science and AI Engineering Solutions and Platforms, and as a technical lead for a team of Data Scientists, Engineers, and Analysts working on end-to-end ML pipelines. You will contribute from problem definition through production deployment, collaborating with cross-functional teams to transform agricultural and business challenges into scalable AI applications.AI Transformation is a Key Priority for Commercial and Regions, and you will have an important role to play transforming the organization to adopt and take the best value of Data Science & AI Solutions and Platforms, working with Data & Insights Team, IT & Digital Team and Business Stakeholders.

Accountabilities

• Design, develop, and deploy production ready AI/ML models and applications that solve critical business problems
• Lead the AI Transformation in the region.
• Influence, educate and evangelize Data Science and AI in the region.
• Drive engagement and adoption of Data Science and AI. Consult in how to shape the Data Science and AI strategy for the region
• Own the complete ML lifecycle: data pipeline design, feature engineering, model training, evaluation, deployment, and monitoring
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions
• Implement MLOps best practices including model versioning, CI/CD pipelines, and automated retraining
• Translate complex business requirements into technical AI/ML solutions with clear success metrics
• Conduct code reviews and establish engineering best practices for AI projects
• Evaluate and integrate emerging AI technologies (LLMs, Generative AI, and RAG systems) into the client’s ecosystem.
• Lead POCs and MVPs using design thinking methodologies to validate solution feasibility
• Optimize model performance, latency, and cost-efficiency for production systems
• Contribute to the client’s AI platform capabilities and reusable component libraries.

Requirements

• Degree in Computer Science, AI, ML, Data Science, Engineering, or related fields.
• 3-5 years of hands‑on experience in AI/ML engineering or related roles
• Proven track record of deploying models to production environments
• Proficiency in Python
• Good understanding of AWS cloud architecture including Sagemaker and Bedrock
• Ability to use identify and re‑use GitHub projects to solve business problems
• Experience working with cross‑functional teams and translating business needs into technical solutions
• Demonstrated ability to manage multiple projects and delivery results in agile environments
• Fluent English, with the ability to communicate effectively with global teams.

Leadership Skills

• Cross‑Functional Technical Leadership: Lead end‑to‑end AI/ML project delivery by orchestrating collaboration between data engineers, business stakeholders, and product teams—translating complex business requirements into scalable technical solutions while establishing clear success metrics and driving alignment across diverse audiences with varying technical backgrounds.
• Engineering Excellence & Mentorship: Drive MLOps best practices and engineering standards across the team through conducting code reviews, establishing reusable component libraries, and guiding junior engineers on production deployment patterns—building organizational AI capability while ensuring technical debt management and architectural excellence.
• Strategic Innovation & Stakeholder Influence: Champion AI‑driven business transformation by leading POCs and MVPs using design thinking methodologies, evaluating emerging technologies (LLMs, GenAI, RAG), and proactively identifying opportunities for AI improvements—securing stakeholder buy‑in through clear communication of technical trade‑offs, ROI potential, and risk mitigation strategies to executive and non‑technical audiences.