Senior Platform Engineer | Developer Experience & AI Native SDLC

About the Role:

We are seeking a Senior Platform Engineer with experience in platform engineering and Developer Experience to help us build the next generation of PicPay's SDLC. This individual will be responsible for creating experiences and capabilities that enable teams to develop and deliver software with greater speed, autonomy, and quality, integrating artificial intelligence into the workflow.

The challenge is to enable an AI-Native operation without turning the process into a “black box.” This means building mechanisms that ensure security, governance, traceability of actions and artifacts generated by AI, while keeping developers capable of understanding, reviewing, questioning, and taking responsibility for decisions made throughout the process.

The ideal candidate combines product and platform vision, solid knowledge of software engineering and DevOps, curiosity to explore new AI applications, and a commitment to simple, reliable, and secure experiences for the development community.

Key Responsibilities:

• Architect, develop, and maintain platforms, frameworks, tools, and automations for the entire software development lifecycle.
• Design and evolve AI-Native experiences to support activities such as planning, development, code review, testing, vulnerability analysis, troubleshooting, documentation, deployment, and operation.
• Create technical guardrails and governance mechanisms for the secure use of AI in the SDLC, including policies, access controls, validations, approvals, and automation limits.
• Ensure traceability of changes, decisions, recommendations, prompts, agents, models, tools, and artifacts generated or modified with the support of artificial intelligence, respecting the organization's security and privacy requirements.
• Develop experiences that foster human understanding, allowing developers to grasp the context, origin, impacts, and criteria behind automated suggestions or actions.
• Integrate Developer Experience solutions into the existing development and operations ecosystem, including CI/CD, version control, Kubernetes, observability, security, and infrastructure as code.
• Evolve delivery pipelines and workflows to be secure by default, auditable, reproducible, and capable of incorporating AI-assisted or AI-executed automations.
• Define and monitor metrics for adoption, efficiency, quality, security, reliability, and developer experience.
• Develop and maintain clear technical documentation, examples, tutorials, usage patterns, and materials that support the adoption of platforms and AI capabilities.
• Work closely with engineering teams to understand needs, validate solutions, identify risks, and transform learnings into product and platform improvements.
• Promote workshops, communities of practice, hackathons, and other forums to educate and engage the technical community.
• Disseminate best practices in engineering, testing, DevOps, security, observability, responsible AI use, and continuous improvement of the SDLC.
• Contribute to the evolution of the area's architecture and processes, participating in strategic technical decisions and the construction of a scalable platform.

Required Skills & Experience:

• Experience with software engineering, platform engineering, technical product development, or related areas.
• Experience in senior positions, working in medium to large teams and complex environments.
• Practical experience with GenAI-based tools to enhance personal engineering productivity and quality.
• Solid knowledge of the software development lifecycle and modern engineering, DevOps, and Platform Engineering practices.
• Experience in building or evolving platforms, internal tools, automations, or products focused on Developer Experience.
• Ability to design solutions considering security, privacy, governance, auditing, traceability, and reliability.
• Ability to critically evaluate AI-produced suggestions and artifacts, identifying technical, security, quality, and compliance risks.
• Strong communication, collaboration, and presentation skills, with the ability to translate complex technical topics for different audiences.
• Ability to think strategically about technology platforms, internal products, and their impact on developer experience.
• Ability to work autonomously and collaboratively, with a proactive approach to investigating problems and delivering results.
• Analytical thinking and problem-solving skills.

Technical Knowledge:

• Solid knowledge of version control and collaboration using GitHub and Trunk-Based Development.
• Experience with static code analysis and tools like SonarQube.
• Experience implementing CI/CD pipelines using tools like Tekton, GitHub Actions, Drone, and/or Jenkins.
• Practical experience with Kubernetes for container orchestration and Argo CD for continuous delivery management.
• Proficiency in at least one of the following languages: Python, Java, JavaScript/TypeScript, PHP, or Go.
• Deep knowledge of DevOps practices, infrastructure as code, and environment automation.
• Familiarity with observability and monitoring practices and tools, such as Grafana, Prometheus, OpenSearch, and Dynatrace.
• Familiarity with load testing practices and tools, such as Locust, k6, Gatling, and JMeter.
• Knowledge of AWS cloud infrastructure and technologies like Docker, Helm Charts, Backstage, Argo CD, and Terraform.

Artificial Intelligence Applied to SDLC:

• Knowledge of the fundamentals of generative artificial intelligence and Large Language Models (LLMs), including tokens, context, embeddings, context windows, temperature, generation limits, and model differences.
• Practical experience integrating applications and platforms with AI models via APIs, SDKs, and model gateways.
• Knowledge of prompt engineering techniques, context structuring, instruction creation, few-shot prompting, and versioned prompt management.
• Knowledge of Retrieval-Augmented Generation (RAG), including document ingestion and chunking, embedding generation, semantic search, reranking, and response grounding.
• Knowledge of building AI-based agents and workflows, including tool calling, external tool usage, permission definition, step orchestration, and failure handling.
• Ability to design AI-assisted development experiences for activities such as code generation and review, test creation, documentation, troubleshooting, log analysis, and deployment automation.
• Knowledge of evaluating AI-based applications, including defining quality criteria, automated testing, evaluation sets, relevance, factuality, security, and regression assessment.
• Knowledge of observability for AI systems, including prompt and response tracking, latency, token consumption, costs, error rate, response quality, and agent behavior.
• Knowledge of security practices for AI applications, including protection against prompt injection, data leakage, sensitive information exposure, misuse of tools, context extraction attacks, and insecure code generation.
• Knowledge of privacy, data management, and access control applied to the use of AI models, considering data classification, environment segregation, information retention, and compliance requirements.
• Knowledge of traceability and auditing mechanisms to record models, versions, prompts, context sources, tools used, automated decisions, and human approvals.
• Ability to implement human-in-the-loop, human review, autonomy levels, approvals, and fallback mechanisms in critical or high-risk workflows.
• Knowledge of strategies to reduce hallucinations and increase response reliability, such as grounding, structured validation, schema usage, deterministic checks, and multi-step review.
• Familiarity with the lifecycle management of AI models and applications, including model selection, versioning, testing, updating, replacement, cost control, and continuous evaluation.
• Familiarity with frameworks and tools for AI application development, evaluation, and observability, such as LangChain, LlamaIndex, Semantic Kernel, LiteLLM, Langfuse, Arize Phoenix, OpenTelemetry, or equivalents.