Senior AI Developer at fa-eups-saasfaprod1.fa.ocs.oraclecloud.com/CX – Sao Paulo | Jobyssey

The Senior AI Engineer is responsible for delivering business value through the design, delivery, and continuous improvement of AI solutions that support core business functions. This role focuses on applying advanced AI engineering techniques, tools, platforms, software engineering, architecture, and MLOps practices to develop scalable, production-ready systems that solve real business problems and contribute to long-term AI Labs capability growth.

As a highly collaborative and delivery-focused senior individual contributor, you will own long-term, multi-person initiatives, including products, frameworks, and technical capabilities, ensuring work is clearly planned, measurable, and aligned to department value. You will work closely with product teams, engineering teams, business stakeholders, and platform consumers to translate business needs into scalable AI solutions, deliver measurable impact, and help others deliver successfully. You will balance technical excellence with strong ways of working, contributing to a culture of accountability, trust, mentorship, and continuous improvement.

This role requires deep applied AI engineering expertise across architecture, software engineering, experimentation, and MLOps, combined with the ability to communicate effectively, mentor others, influence technical direction, communicate trade-offs, and drive business-aligned outcomes through collaboration and leadership

We offer flexibility in how and where you work. This position can be based remotely in all the Brazil region.

AI Solution Delivery & Ownership

• Lead the design, delivery, and continuous improvement of scalable AI solutions, products, frameworks, and capabilities that deliver measurable business value.
• Translate business needs into production-ready AI systems and enterprise integrations with clear outcomes and success metrics.

Applied AI Engineering

• Develop and implement AI solutions using LLMs, RAG, agentic frameworks, document intelligence, machine learning, and data science techniques.
• Evaluate and apply appropriate AI technologies, patterns, and tools to maximize business impact and maintainability.

Software Engineering & Architecture

• Design, develop, and maintain scalable, cloud-native, and enterprise-grade applications using modern software engineering practices, clean architecture, reusable design patterns, and comprehensive testing.
• Define and evolve engineering standards, frameworks, and reference architectures while providing technical leadership on solution design, architecture decisions, and engineering best practices.

Cloud, DevOps & MLOps

• Design, deploy, and operate scalable AI platforms and solutions on Azure using modern DevOps and MLOps practices, including CI/CD, Infrastructure as Code (IaC), containerization, automation, and cloud-native architectures.
• Manage the full lifecycle of AI applications, platforms, and models, including deployment, monitoring, observability, performance optimization, reliability, release management, and operational support.
• Implement and maintain secure, resilient, and observable production environments through monitoring, logging, alerting, governance, and infrastructure best practices.

Evaluation, Experimentation & Continuous Improvement

• Design and execute testing, evaluation, and experimentation strategies to validate solution effectiveness and business value.
• Continuously improve AI solutions through performance measurement, monitoring, feedback, and iterative optimization.

Governance, Security & Responsible AI

• Ensure AI solutions adhere to security, privacy, governance, compliance, operational, and Responsible AI requirements.
• Design solutions that are secure, reliable, scalable, and aligned with enterprise standards.

Collaboration, Leadership & Ways of Working

• Collaborate with product, engineering, platform, and business teams to deliver AI solutions aligned with organizational objectives and measurable business outcomes.
• Communicate technical concepts effectively, provide technical leadership and mentorship, and influence solution direction through collaboration and engineering best practices.
• Demonstrate strong ownership, accountability, adaptability, and commitment to UL Solutions Ways of Working while fostering a culture of trust, innovation, continuous learning, and continuous improvement.

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, Data Analytics, or a related field.
• 5+ years of experience developing software, AI, machine learning, cloud, or data-driven solutions.
• Strong Python software engineering experience using modern development practices.
• Experience designing and delivering scalable, secure, and maintainable software and AI solutions.
• Experience building and deploying AI and machine learning solutions into production.
• Experience with cloud platforms, preferably Microsoft Azure.
• Experience with Azure DevOps, CI/CD, Infrastructure as Code (IaC), MLOps, and deployment automation.
• Experience developing distributed systems, APIs, and enterprise integrations.
• Experience with Docker, Kubernetes (K8s), and containerized application deployment.
• Experience with LLMs, RAG, document intelligence, intelligent automation, or related AI technologies.
• Strong understanding of software architecture, engineering best practices, and enterprise security considerations.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Demonstrated ability to lead technical initiatives and work effectively in Agile environments.

Preferred Qualifications

• Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline.
• Experience designing enterprise AI platforms, architectures, frameworks, or shared services.
• Experience with generative AI technologies, including LLMs, RAG, agentic AI, and document intelligence solutions.
• Experience implementing AI evaluation, experimentation, and performance measurement frameworks.
• Experience mentoring engineers and providing technical leadership.
• Knowledge of responsible AI, governance, compliance, and security best practices.
• Azure certifications.
• Experience delivering AI solutions in large-scale enterprise environments.

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