Senior AI Engineer & Delivery Lead

Recrutei; São Paulo; On-site; BRL 220,000 - 360,000; Full time

Job summary

Maxxi is hiring a Senior AI Engineer & Delivery Lead to join the Data & Insights team in São Paulo. You will build scalable AI/DS solutions and lead a cross-functional team of data scientists, engineers and analysts from problem framing to production deployment.

In this hands-on role you will optimize models for performance and cost, drive MLOps adoption, and influence the regional AI strategy while collaborating with global teams.

Qualifications

• Degree in CS/AI/ML, or engineering field.
• 3–5 years hands-on AI/ML engineering experience.
• Proven production deployment of ML models.
• Fluent English; strong cross-functional collaboration.

Responsibilities

• Design, develop and deploy production-ready AI/ML models.
• Lead end-to-end ML lifecycle: data pipelines, training, evaluation, deployment.
• Implement MLOps practices including CI/CD and retraining.
• Coach a diverse team of data scientists and engineers.
• Translate business needs into scalable AI solutions.
• Evaluate emerging AI technologies and drive regional adoption.

Skills

Python

MLOps

Agile

Leadership

Cross-functional

Education

CS/AI/ML degree

Tools

SageMaker

Bedrock

GitHub

Job description

Maxxi is hiring a Senior AI Engineer & Delivery Lead to join the Data & Insights team of a global agribusiness company.

You will support the client’s AI transformation by identifying and developing scalable Data Science and AI solutions that improve commercial and regional processes and deliver measurable business impact.

In this role, you will work both as a hands-on engineer, building Data Science and AI engineering solutions and platforms, and as the technical lead of a team of data scientists, engineers and analysts working on end-to-end ML pipelines. You will contribute from problem definition through production deployment, working with cross-functional teams to turn agricultural and business challenges into scalable AI applications.

Accountabilities

• Design, develop and deploy production-ready AI/ML models and applications that solve critical business problems.


• Own the complete ML lifecycle: data pipeline design, feature engineering, model training, evaluation, deployment and monitoring.


• Implement MLOps best practices, including model versioning, CI/CD pipelines and automated retraining.


• Conduct code reviews and establish engineering best practices for AI projects.


• Work with cross-functional teams to translate complex business requirements into technical AI/ML solutions with clear success metrics.


• Evaluate and integrate emerging AI technologies (LLMs, Generative AI and RAG systems) into the client’s ecosystem.


• Lead POCs and MVPs using design thinking to validate solution feasibility.


• Optimize model performance, latency and cost-efficiency in production systems.


• Contribute to the client’s AI platform capabilities and reusable component libraries.


• Lead the AI transformation in the region: influence, educate and evangelize Data Science and AI across the business.


• Drive engagement with and adoption of Data Science and AI, and advise on the Data Science and AI strategy for the region.

Requirements

• Degree in Computer Science, AI, ML, Data Science, Engineering or a related field.


• 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 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 deliver 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, coordinating data engineers, business stakeholders and product teams, setting clear success metrics and building alignment across audiences with different technical backgrounds.


• Engineering excellence and mentorship: drive MLOps practices and engineering standards across the team through code reviews, reusable component libraries and hands‑on guidance to junior engineers on production deployment, while managing technical debt and keeping the architecture sound.


• Strategic innovation and stakeholder influence: champion AI-driven business transformation by leading POCs and MVPs, evaluating emerging technologies (LLMs, GenAI, RAG) and identifying opportunities for AI, securing stakeholder buy‑in by clearly communicating technical trade‑offs, ROI potential and risks to executive and non-technical audiences.