Staff AI Engineer (AI Labs)

Location: Hybrid in Madrid, Barcelona, Bucharest, Montevideo, Sao Paulo, or Buenos Aires

Company

hirify.global provides financial infrastructure for global commerce and payments across more than 60 emerging markets.

What you will do

• Scout, prototype, benchmark, and evaluate emerging AI models, tools, frameworks, agentic systems, vector databases, copilots, and assistants.
• Design evaluation environments with datasets, prompts, scenarios, telemetry, regression tracking, and reproducible benchmark coverage.
• Build focused prototypes to assess architecture, integration patterns, security boundaries, operational constraints, reliability, and failure modes.
• Compare vendor and open-source solutions across quality, cost, latency, security, integration complexity, and vendor lock-in; produce adoption recommendations and decision memos.
• Define readiness guidance, guardrails, standards, risk mitigations, and integration requirements, then coordinate hand-offs to production engineering teams.
• Collaborate with Security, Legal, Compliance, AI, platform, product, and operations teams; mentor engineers and contribute to hiring, internal write-ups, talks, and meetups.

Requirements

• 8+ years of software engineering experience, including significant experience at senior or Staff-level scope.
• Deep hands-on experience building and evaluating LLM-based systems and modern AI tooling, including agentic or multi-step systems with tool use, orchestration, state, retrieval, or external integrations.
• Strong software engineering fundamentals and the ability to rapidly build high-quality experimental systems.
• Strong cloud infrastructure knowledge, preferably AWS, with the ability to run experimental workloads securely and cost-consciously.
• Experience with observability, telemetry, testing, benchmarking, system architecture, reliability, scalability, asynchronous workflows, and distributed components where relevant.
• Experience designing experiments and benchmarks that compare AI models and tools under real constraints and influence technical decisions.
• Experience constructing evaluation datasets, including task selection, labelling, holdout discipline, and maintaining useful datasets as models improve.
• Working knowledge of LLM-as-judge methods and failure modes, human evaluation, inter-annotator agreement, statistical significance on small samples, regression tracking, telemetry, and versioning.
• Ability to create scoped evaluation plans with hypotheses and metrics, make trade-off decisions, write concise decision memos, explain results to non-specialists, and influence teams without formal authority.
• Curiosity, disciplined measurement, risk awareness, self-directed work in a small high-leverage team, and a preference for reusable tools, templates, and playbooks.

Culture & Benefits

• Flexible schedules focused on impact and productivity rather than fixed hours, combining self-managed focus time with in-person collaboration hubs depending on role and location.
• Fintech environment with opportunities to build and work on evolving payment infrastructure.
• Country-specific benefits and an internal referral bonus program.
• Work from anywhere while traveling for up to 3 months per year.