Staff AI Engineer – AI Labs

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• Validate high-value emerging AI and automation technologies and de-risk their adoption across dLocal
  
• Own technology scouting, prototyping, and evaluation for dLocal
  
• Run instrumented spikes and benchmarks on LLMs, agentic systems, vector databases, orchestration frameworks, copilots, assistants, and other emerging technologies
  
• Compare vendor and open-source options across quality, cost, latency, security, and integration complexity
  
• Deliver decision memos with recommendations to adopt, watch, or avoid
  
• Design and maintain evaluation environments with datasets, prompts, scenarios, and telemetry
  
• Build automation and tooling to measure quality, robustness, latency, cost, and regressions
  
• Build focused prototypes to explore architecture, integration patterns, operational constraints, security boundaries, and failure modes
  
• Define readiness guidance, patterns, guardrails, limitations, operational considerations, and integration requirements
  
• Coordinate hand-offs to engineering teams responsible for productionization and support transitions as needed
  
• Track outcomes of Lab recommendations to improve evaluation methods
  
• Work with Security, Legal, Compliance, and AI teams on risk assessments and governance recommendations
  
• Maintain reusable checklists, decision templates, and standards
  
• Incorporate learnings from external copilots and the AWS AI suite into adoption guidelines
  
• Partner with AI and domain teams to ensure collaboration and clear boundaries
  
• Participate in hiring as a technical evaluator and culture champion
  
• Mentor engineers on evaluation methods, benchmarking, and experimental design
  
• Share knowledge through internal write-ups, tech talks, meetups, and conferences
  

Requirements

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• 8+ years of software engineering experience, including significant experience operating at senior or Staff-level scope
  
• Deep hands-on experience building and evaluating systems based on LLMs and modern AI tooling
  
• Strong software engineering fundamentals and ability to rapidly build high-quality experimental systems
  
• Experience building agentic or multi-step AI systems involving tool use, orchestration, state, retrieval, or external integrations
  
• Strong knowledge of cloud infrastructure, preferably AWS, and ability to run experimental workloads securely and cost-consciously
  
• Experience with observability, telemetry, testing, and benchmarking of complex systems
  
• Ability to reason about system architecture, reliability, scalability, asynchronous workflows, and distributed components
  
• Track record of designing experiments or benchmarks that influenced meaningful technical decisions
  
• Experience constructing evaluation datasets, including task selection, labelling, and holdout discipline
  
• Working knowledge of LLM-as-judge methods, human evaluation, inter-annotator agreement, and their appropriate use
  
• Ability to reason about statistical significance on small samples
  
• Familiarity with regression tracking, telemetry, and versioning
  
• Ability to turn ambiguous ideas into scoped evaluation plans with hypotheses and metrics
  
• Comfortable making trade-off calls across quality, latency, cost, and vendor lock-in
  
• Experience writing concise decision memos
  
• Ability to explain technical results to non-specialists
  
• Experience working with platform, product, and operations teams
  
• Ability to influence without authority and align teams around standards and guardrails
  
• Curious, experimentation-oriented mindset with disciplined measurement and risk awareness
  
• Comfortable in a small, high-leverage team without embedded PMs
  
• Builder attitude favoring reusable tools, templates, and playbooks
  

Core Competencies

Demonstrates extensive experience in evaluating and adopting AI and automation technologies, with a strong focus on LLMs and cloud infrastructure, particularly xcskxlj AWS. Capable of designing experiments, building prototypes, and collaborating across teams to ensure effective integration and governance.

Highest-signal resume keywords

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• LLM Evaluation
  
• Cloud Infrastructure (AWS)
  
• Experimental Design
  
• Benchmarking and Telemetry
  
• Decision Memo Writing
  

Hard Skills

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• Software Engineering
  
• AI Tooling
  
• System Architecture
  
• Observability
  
• Statistical Significance Reasoning
  
• Evaluation Dataset Construction
  
• Regression Tracking
  
• Integration Patterns
  
• Automation and Tooling
  
• Prototyping
  

Soft Skills

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• Influencing Without Authority
  
• Curiosity
  
• Collaboration
  
• Mentoring
  
• Communication
  

Industry Keywords

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• AI Governance
  
• Risk Assessment
  
• Compliance
  
• Experimental Workloads
  
• Vendor Evaluation
  

Tools & Technologies

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• LLMs
  
• Agentic Systems
  
• Vector Databases
  
• Orchestration Frameworks
  
• Telemetry Tools
  
• Decision Templates
  
• Reusable Checklists
  
• AWS AI Suite
  

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