Staff Engineer (AI Native)

Accountabilities

• Define AI-native engineering standards: Establish verifiable specifications, including preconditions, postconditions, invariants, and Given/When/Then scenarios. Maintain the instructions, context, skills, and constraints that guide AI agents.

• Orchestrate AI agents: Break complex engineering challenges into manageable, independently verifiable tasks. Coordinate multiple agents, isolated worktrees, and handoffs between agents and human engineers.

• Establish delegation policies: Partner with engineering teams to determine which tasks are suitable for AI delegation and which require human judgment, particularly for product design, strategic decisions, and organizational context.

• Strengthen verification practices: Design testing strategies, evaluations, mutation testing, and benchmarks to validate AI-generated code. Guide code reviews toward intent, correctness, and risk rather than code volume alone.

• Implement deterministic guardrails: Maintain rigorous CI/CD pipelines, branch protection, CODEOWNERS policies, small delivery batches, and straightforward rollback mechanisms to support safe and reliable releases.

• Embed security into agent workflows: Design secure agent identities, least-privilege access, sandboxing, and defense-in-depth controls in collaboration with information security teams.

• Develop the shared engineering harness: Continuously improve shared instructions, tools, hooks, and skills, using lessons from failures to strengthen workflows across teams.

• Lead production reliability: Define progressive autonomy levels for agents in production, incorporating authorization controls, dry runs, emergency stop mechanisms, service-level objectives (SLOs), observability, and contingency plans.

• Apply AI to operational workflows: Use AI where it adds measurable value, including operational playbooks, anomaly detection, root cause analysis, postmortem drafting, and incident handoffs, while preserving effective conventional automation.

• Mentor and develop engineers: Coach team members, particularly junior and mid-level engineers, and promote deliberate practice of core software engineering fundamentals to prevent overreliance on AI tools.

• Communicate technical strategy: Translate AI capabilities, limitations, risks, and business benefits into actionable insights for technical leadership and business stakeholders, supporting decisions with measurable evidence.

Requirements

• Extensive software engineering experience, with a track record as a technical authority in a Staff Engineer, Principal Engineer, or Tech Lead capacity within high-scale production systems.

• Strong foundations in software architecture, data structures, invariants, system boundaries, and security principles, with the ability to identify subtle defects in AI-generated code.

• Demonstrated, intensive use of AI agents in everyday software development, including expertise in context engineering and specification-driven development.

• Practical experience with test-driven development (TDD), automated testing, and evaluation methods, including the design of verification strategies for AI-generated solutions.

• Hands-on experience with CI/CD, version control, branch protection, and incremental delivery practices.

• Experience operating production systems, including observability, SLOs, incident response, root cause analysis, and postmortems.

• Applied security expertise covering least privilege, isolation, identity management, and secrets management.

• Strong technical judgment and calibrated skepticism, knowing when to trust AI-assisted workflows, when to verify results, and when human intervention is essential.

• Systems thinking and risk assessment skills, with a focus on measurable business outcomes, user impact, and operational reliability.

• Excellent communication skills across engineering teams, technical leadership, and business stakeholders, together with a genuine commitment to mentoring.

• Nice to have: Experience orchestrating multiple AI agents and maintaining shared engineering harnesses across teams.

• Nice to have: Background in Site Reliability Engineering (SRE) or platform engineering, including production agent governance and continuous evaluations such as LLM-as-a-Judge.

• Nice to have: Experience with mutation testing, performance benchmarks, or resilience testing.

• Nice to have: Publicly shared projects built with AI agents, particularly those demonstrating strong security practices and resistance to attacks.

• Nice to have: Experience in payments, fintech, telecommunications, or other high-volume transaction environments.

• Nice to have: A proven history of mentoring engineers and developing technical learning programs.

Benefits

• National Bradesco health insurance, extended to dependents without an additional per-dependent charge.

• Optional Bradesco dental insurance.

• Flexible meal and food allowance, maintained during vacation periods.

• Profit-sharing program (PLR).

• Wellhub wellness and fitness benefits.

• Birthday day off.

• Home office allowance.

• Public transportation allowance when applicable, subject to legally permitted deductions.

• Zurich life insurance.

• Complimentary access to company products, including AppsClub, Clube de Descontos, TrueCaller, BTFit, and Busuu.

• Access to internal training and digital learning platforms.

• BeInspired employee recognition program.

• Remote work flexibility and the opportunity to influence AI-native engineering practices across teams.

• Professional growth through technical leadership, mentoring, and work on advanced AI-assisted development and production reliability challenges.

• An inclusive workplace, with the opportunity open to people with disabilities.