Senior AI Engineer, Agentic SDLC

New

JobgetherAI software engineering

Based in BrazilFull-TimeSenior

Salary not disclosed

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Job Details

Have strong professional software engineering experience with Java, Python, or comparable programming languages.
Be familiar with CI/CD pipelines and DevOps practices, including Azure DevOps, GitFlow, and SonarQube.
Have cloud computing experience, particularly with AWS Lambda, SQS, and S3.
Have practical experience using generative AI tools for software development, such as Claude Code or GitHub Copilot.
Be able to write and refine prompts, configure AI agents, and assess the quality, accuracy, and usefulness of their outputs.
Understand software quality practices, including automated testing, behavior-driven development (BDD), and static application security testing (SAST).
Identify technical risks and develop reliable automation solutions.
Demonstrate attention to code quality, security, compliance, and responsible AI implementation.
Collaborate effectively with technical teams and communicate engineering decisions clearly.
Preferred: experience with agentic development frameworks or methodologies such as AISDD or Spec-Kit.
Preferred: knowledge of microservices architecture and frameworks such as Spring Boot or Quarkus.
Preferred: familiarity with workflow observability and orchestration tools such as Flow Ops or Databricks.
Preferred: experience delivering large-scale projects involving multiple engineering teams or squads.
Preferred: experience in employee benefits, financial technology, or fintech.

Configure generative AI platforms, including agent skills, personas, prompts, and contextual information.
Develop, maintain, and optimize agentic workflows integrated with Azure DevOps, Git, and CI/CD tools.
Integrate AI agents into requirements analysis, refinement, implementation, testing, and deployment.
Tune agent parameters, prompting strategies, and execution workflows to improve performance and output quality.
Plan the AI infrastructure roadmap with the AI Orchestrator and other technical stakeholders.
Automate software development activities while maintaining engineering quality, security, and compliance standards.
Integrate quality assurance and security controls, including SonarQube, Veracode, automated test coverage, and related safeguards.
Assess and mitigate technical risks associated with deploying AI agents in production.
Evaluate agent outputs and refine workflows for reliability, consistency, and alignment with engineering requirements.
Ensure AI solutions adhere to applicable ethical principles, regulatory requirements, and organizational standards.
Collaborate with development teams and technical stakeholders to improve AI-enabled engineering workflows.

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