Principal AI-Native Engineer, Ionic Partners in Brazil at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal AI-Native Engineer based in Brazil.

This is a high-impact principal-level engineering role focused on building AI-native systems that operate real business functions in production.
You’ll design, build, deploy, and continuously improve agentic systems rather than prototypes, pilots, or strategy documents.
Working across a diverse portfolio, you’ll partner with domain experts to turn complex functional requirements into reliable autonomous systems.
You’ll shape agent architectures, orchestration, context systems, evaluation frameworks, integrations, observability, and operational safeguards.
The role offers significant technical ownership, with responsibility for system performance, reliability, cost, maintenance, and long-term outcomes.
You’ll work in a deliberately flat, globally distributed environment where influence comes from what you build and the standards you establish.
This is an opportunity to remain deeply technical while working at the forefront of production-grade agentic engineering.

• Partner with subject-matter experts across different business functions to translate functional requirements into robust agentic system designs, challenging assumptions and identifying failure modes before implementation.
• Design agent architectures covering boundaries, orchestration, control flow, tools, state and memory, human-in-the-loop processes, and the balance between model-driven and deterministic logic.
• Build, deploy, operate, and continuously improve production agentic systems that perform real business functions end to end.
• Extend and strengthen internal orchestration frameworks, including execution control, integrations, permissions, observability, failure handling, and recovery mechanisms.
• Improve automated development and QA agents integrated into production engineering workflows.
• Create and maintain structured, machine-readable context artifacts covering products, customers, processes, and operational data.
• Design retrieval and context-assembly strategies, manage context budgets, and apply disciplined, versioned, tested approaches to prompting.
• Integrate agentic systems with production platforms including codebases, CRMs, ERPs, support systems, data warehouses, and internal services.
• Implement authentication, authorization, rate limits, cost controls, idempotency mechanisms, and blast-radius protections for autonomous systems.
• Define success criteria and build evaluation harnesses, baselines, regression suites, and human-review processes for non-deterministic systems.
• Implement tracing and observability capabilities that make production agent behavior measurable and diagnosable.
• Build appropriate guardrails, fallbacks, retries, circuit breakers, approval gates, audit trails, and rollback mechanisms.
• Monitor and optimize token usage, tool costs, system performance, evaluation trends, and unit economics at production scale.
• Investigate incidents, regressions, quality drift, and unexpected system behavior, taking ownership of remediation and long-term improvements.
• Evolve deployed systems as the business processes and requirements they support change.
• Quickly understand and safely extend complex production codebases across different businesses and technical environments.
• Extract reusable components, abstractions, and engineering patterns so recurring problems become faster and more cost-effective to solve.
• Evaluate emerging models, frameworks, agentic techniques, and development tools against real workloads and make adoption decisions based on measurable results.
• Establish responsible autonomous-operation practices covering data exposure, intellectual property, security boundaries, and human review requirements.
• Review agentic systems developed elsewhere and raise technical standards through direct feedback, collaboration, and demonstrated engineering outcomes.
• Work closely with engineers and business operators whose workflows are being transformed, clearly communicating system behavior, limitations, and intent.
• Retire systems, architectures, or approaches that no longer deliver sufficient value and document the reasoning behind those decisions.

• Bachelor’s degree or higher in Computer Science, Computer Engineering, Software Engineering, or a related field, or equivalent practical experience. Demonstrated engineering achievements and shipped systems are valued more heavily than formal credentials.
• 8+ years of hands-on software engineering experience building, deploying, and operating production systems.
• 3+ years of experience building AI-native or agentic systems that reached production and were used for meaningful real-world work, beyond prototypes, demos, hackathons, or pilot evaluations.
• Demonstrated end-to-end ownership of systems, including design, development, deployment, operations, maintenance, improvement, and accountability for failures.
• Experience integrating AI systems with production business platforms such as codebases, CRMs, ERPs, support and ticketing systems, data warehouses, and internal services.
• Strong understanding of authentication, authorization, permissions, and controls governing autonomous systems.
• Experience designing and operating evaluations for non-deterministic AI systems, including correctness criteria, evaluation harnesses, baselines, regression testing, and human review.
• Experience operating LLM-based systems in production, including tracing, failure diagnosis, guardrails, reliability engineering, and cost management at scale.
• Proven ability to become productive quickly within large and complex codebases that you did not originally build.
• Experience translating specifications from domain experts outside your own technical specialization into production-grade engineering solutions.
• Experience with platforms, infrastructure, developer tooling, or shared technical frameworks that other engineers or systems depend on.
• Strong ownership mindset, treating shipped systems as an ongoing responsibility encompassing outcomes, reliability, costs, failures, and maintenance.
• High autonomy and the ability to operate from intent, establish priorities, and make progress through genuine ambiguity without requiring detailed instructions.
• Strong intellectual honesty, including the ability to use data objectively, acknowledge failures, and retire systems when they no longer justify their cost or complexity.
• Excellent technical judgment when balancing speed, reliability, autonomy, safety, maintainability, and engineering complexity.
• Adaptability and curiosity, with an expectation that AI techniques and tools will evolve rapidly and a willingness to continuously test new approaches.
• Strong collaboration skills when working with domain experts, engineers, and operators across different areas of expertise.
• Excellent written and verbal communication skills, with the ability to explain complex system behavior clearly to both technical and non-technical audiences.
• High standards, disciplined execution, and a low-ceremony approach to engineering excellence.
• Experience with enterprise agentic systems, legacy environments, compliance requirements, security reviews, sensitive data, or organizational change is advantageous.
• Experience making business functions such as support, finance, marketing, or revenue operations agentic is a plus.
• Experience building shared platforms or frameworks, working with enterprise SaaS or ERP systems, modernizing legacy applications, or applying AI-assisted development and QA workflows is desirable.
• Experience working across multiple companies, business units, or product lines and within fully remote, globally distributed organizations is beneficial.
• Open-source contributions, technical writing, or public work related to agentic systems is a plus.

• 100% remote and globally distributed work environment.
• Flexible working hours with an asynchronous-first approach focused on outcomes rather than online presence.
• Access to frontier AI models, agentic tooling, and infrastructure with significant technical autonomy in evaluating and selecting technologies.
• Opportunity to inherit and extend a live production agentic platform rather than starting from scratch.
• Broad exposure to different companies, business functions, technical environments, and problem domains.
• Principal-level individual contributor career path with substantial scope and autonomy, without pressure to transition into management.
• Opportunity to build systems that directly transform how real businesses operate.
• Consistent feedback, career-development support, and access to leading-edge tools, playbooks, and technology.
• Opportunities to connect with thought leaders and participate in webinars focused on emerging technology.
• Home-office setup stipend to support an effective remote working environment.
• Culture-building activities including coffee chats, happy hours, cooking classes, book clubs, and other community events.
• Commitment to keeping candidates informed throughout the recruitment process, with interviewed applicants notified of hiring decisions within 45 days of their interview.