Staff Platform Engineer | Developer Experience & AI Native SDLC

About the Area:

The Developer Experience team exists to transform PicPay's software development lifecycle into an AI Native operation, enabling our tech community to deliver more value with speed, quality, and confidence.

Our mission is to evolve the SDLC into a model where artificial intelligence, automation, internal platforms, and engineering best practices work together to remove obstacles, increase developer productivity, and improve the end-to-end experience—without compromising on security, traceability, and human understandability at every stage of the process.

We work on topics such as software catalogs, developer portals, common libraries, source code management and builds, dependency management, testing tools, continuous integration, continuous deployment, technical governance, observability, intelligent automations, and AI-assisted experiences for software development, review, documentation, operation, and evolution.

About the Job:

We are looking for a Staff Platform Engineer with solid experience in platform engineering, Developer Experience, and development lifecycle transformation using applied AI.

This person will face the challenge of defining technical directions, influencing architectural decisions, and technically leading the evolution of PicPay's Developer Experience platform toward an AI Native model. The role combines technical depth, systems thinking, cross-functional influence, a product mindset for developers, and responsibility for solutions that balance productivity, security, traceability, governance, and human comprehension.

The ideal candidate must have experience turning complex, recurring engineering problems into reusable platforms, standards, tools, and capabilities for the entire organization. We expect someone capable of operating autonomously in ambiguous domains, bridging strategy and execution, mentoring other engineers, and creating safe pathways for AI adoption across the SDLC at scale.

Responsibilities and Assignments:

• Define, in partnership with technical leadership and peer teams, the evolution strategy for the Developer Experience platform toward a safe, traceable, and understandable AI Native SDLC.
• Architect and technically lead the development of frameworks, platforms, integrations, and automations that enhance the software development lifecycle, including build, testing, code analysis, review, documentation, deployment, observability, governance, and AI-assisted operations.
• Establish technical standards, architectural principles, and guardrails for AI adoption across the SDLC, ensuring productivity without compromising security, quality, traceability, compliance, or human review capability.
• Influence the evolution of PicPay's engineering ecosystem by connecting the needs of product, security, architecture, infrastructure, SRE, data, compliance, and development teams.
• Design and evolve platform experiences that help developers write, review, test, document, deliver, and operate software with greater efficiency, confidence, and autonomy.
• Technically lead integrations between development tools, internal platforms, CI/CD pipelines, observability systems, software catalogs, AI agents, model APIs, and governance mechanisms.
• Define traceability and auditing practices for AI Native solutions, including versioning of prompts, models, context sources, automated actions, agent decisions, and human approvals.
• Guide the adoption of AI Engineering practices, including context engineering, RAG, evals, AI observability, hallucination mitigation, prompt injection defense, sensitive data protection, and human-in-the-loop workflow design.
• Act as a technical reference for other engineers by providing mentorship, solution reviews, knowledge sharing, and continuous improvement of engineering standards.
• Build and maintain strategic and technical documentation, guides, code samples, tutorials, and educational materials that facilitate the adoption of platforms, tools, and practices created by the team.
• Organize and lead workshops, internal communities, technical sessions, and decision forums to educate, align, and engage the development community around the AI Native SDLC transformation.
• Advocate for code quality, security, efficiency, observability, and operational excellence by promoting solid testing, automation, code review, continuous delivery, and technical governance practices.

Requirements and Qualifications:

• Proven experience technically leading high-impact initiatives, with influence across multiple teams, shared platforms, or organization-critical architectural decisions.
• Hands-on experience in Platform Engineering, Developer Experience, DevOps, SRE, software architecture, or environments focused on improving developer experience.
• Consistent hands-on practice using GenAI-based tools to enhance personal engineering productivity, including development, code analysis, testing, documentation, troubleshooting, solution design, and technical review.
• Deep understanding of software development best practices, application lifecycles, systems architecture, quality, security, observability, reliability, and production operations.
• Ability to navigate ambiguous problems, break down complex challenges, propose sustainable technical strategies, and drive decisions with organizational impact.
• Ability to think strategically about developer-facing technology products, balancing user experience, scalability, governance, security, cost, and business impact.
• Ability to influence without formal authority, build alignment across different teams, and translate complex technical problems into clear decisions for diverse audiences.
• Ability to mentor other engineers technically, raising engineering standards, supporting architectural decisions, and disseminating knowledge.
• Experience in regulated or financial environments with high security, auditing, and traceability requirements is considered a strong differentiator.

Technical Knowledge:

• Advanced knowledge of internal platform architecture, distributed systems design, APIs, automation, tool integrations, and building reusable capabilities for engineering teams.
• Solid knowledge of version control and collaboration using GitHub, pull requests, code reviews, branching strategies, and Trunk-Based Development.
• Experience with static code analysis, quality management, automated policies, and tools such as SonarQube or equivalents.
• Advanced experience in designing, implementing, and evolving CI/CD pipelines using tools such as Tekton, GitHub Actions, Drone, Jenkins, or equivalents.
• Hands-on experience with Kubernetes for container orchestration and Argo CD for continuous delivery management.
• Solid programming skills in at least one of the following languages: Python, Java, JavaScript/TypeScript, PHP, or Go.
• Deep understanding of DevOps practices, Infrastructure as Code, environment automation, and scalable platform design.
• Familiarity with observability and monitoring practices and tools, such as Grafana, Prometheus, OpenSearch, Dynatrace, or equivalents.
• Familiarity with load testing practices and tools, such as Locust, k6, Gatling, JMeter, or equivalents.
• In-depth knowledge of AWS cloud infrastructure and technologies such as Docker, Helm Charts, Backstage, Argo CD, and Terraform.
• Applied knowledge of GenAI and LLM fundamentals, including tokens, context windows, embeddings, temperature, language models, limitations, risks, costs, latency, and trade-offs across different models.
• Experience or familiarity with integrating applications and platforms with AI models via APIs, SDKs, model gateways, internal tools, or market providers.
• Knowledge of prompt engineering and context engineering, including prompt versioning, reusable templates, few-shot prompting, system instructions, input structuring, grounding, and output validation.
• Knowledge of RAG, including data ingestion, chunking, embeddings, semantic search, reranking, grounding, source citation, context access control, and hallucination reduction.
• Familiarity with AI agents, tool calling, autonomous or semi-autonomous workflows, task orchestration, permissions definition, autonomy boundaries, error handling, and human supervision mechanisms.
• Knowledge of AI system evaluations, including building evals, test datasets, quality metrics, relevance, factuality, security, regression, model comparison, and continuous production evaluation.
• Familiarity with AI observability, including tracing prompts and responses, latency, token usage, costs, errors, perceived quality, agent behavior, and automated decision auditing.
• Knowledge of AI security, including prompt injection, data leakage, sensitive data exposure, tool misuse, context extraction, insecure code generation, and access governance.
• Understanding of privacy, data classification, secrets management, permissions management, and best practices for AI usage in corporate and regulated environments.
• Knowledge of traceability and auditability for AI Native solutions, including version logging of models, prompts, context sources, executed tools, automated decisions, and human approvals.
• Familiarity with human-in-the-loop practices, safe fallbacks, human review, operational explainability, and designing experiences where automation assists decisions without removing human comprehension.
• Familiarity with tools and frameworks such as LangChain, LlamaIndex, Semantic Kernel, LiteLLM, Langfuse, Arize Phoenix, OpenTelemetry, or equivalents is considered a differentiator.

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