Platform Engineer

• Design and implement tools and technologies to provision and configure an enterprise software system hosted in Kubernetes
• Design and implement architecture and networking solutions within Azure
• Automate/script existing processes using languages such as python and bash
• Provision, configure, and maintain cloud resources using Terraform and Terragrunt
• Actively monitor the application environment and respond to incidents
• Conduct root cause analysis for production incidents
• Oversee cloud infrastructure and recommend any potential improvements to technology or process
• Contribute to team planning to solve engineering challenges
• Be a dependable and highly skilled development resource for peers through education and review
• Have a broad awareness of related projects and industry trends, and encourage innovative practices among peers
• Provide detailed feedback and suggestions to team code reviews
• Document and demonstrate solutions through written documentation, diagrams, and readable code

• Design, build, and operate the corporate AI Gateway using Azure API Management (APIM).
• Develop advanced governance, authentication, routing, and observability policies for generative AI workloads.
• Integrate multiple AI providers, including Azure OpenAI, Azure AI Foundry, GCP Vertex AI, AWS Bedrock, and Adobe Firefly.
• Implement FinOps mechanisms to control consumption, per-subscription quotas, token budgeting, and cost attribution.
• Develop and maintain infrastructure as code (IaC) using Terraform/OpenTofu.
• Build and evolve CI/CD pipelines with GitHub Actions using OIDC authentication.
• Create centralized observability mechanisms using Application Insights, KQL, Azure Workbooks, Datadog, and CloudWatch.
• Develop APIM policies for SSE streaming, request/response transformation, retry, fallback, and backend routing.
• Work on platform security using Azure AD, JWT validation, WAF tuning, Front Door, and Key Vault.
• Automate operations and administrative workflows using Bash, PowerShell, and Python.
• Produce technical documentation, OpenAPI specs, and materials for technical and non-technical stakeholders.
• Support internal teams in the secure and scalable adoption of generative AI.
• Translate technical limitations and architectural decisions into clear recommendations for different audiences.
• Work autonomously to identify needs, prioritize improvements, and continuously evolve the platform.

Lead AI Platform Engineer

• Design and implement ETL pipelines capable of processing terabytes of data efficiently.
• Develop and optimize APIs to support fast and reliable data retrieval, including both exact and partial matches.
• Architect and manage scalable storage solutions (e.g., Bigtable, BigQuery, Elasticsearch, or other NoSQL/SQL systems).
• Collaborate with cross-functional teams to turn raw data into actionable products that drive insights and business value.
• Work closely with both internal and external stakeholders, including data science, product, and operations teams.
• Document processes, architectures, and APIs; mentor junior engineers and foster a culture of technical excellence.

Data Platform Engineer

• Lead the technological future of the platform, evaluating trade-offs between adopting new solutions and optimizing legacy systems to ensure scalability in multi-region environments.
• Support the team in architectural decisions aligned with Creditas’ overarching engineering strategy, ensuring resilience and high-availability standards.
• Define and maintain governance standards, ensuring created abstractions (such as the CDitas CLI) streamline the developer experience without generating technical debt.
• Lead initiatives focused on reducing manual operations, increasing development velocity through automation and platform reliability.
• Propose viable technical solution alternatives, ensuring decisions are made in a timely manner so the team’s progress is not blocked.
• Engage with stakeholders to translate product challenges into platform solutions that reduce delivery time and operational cost (KTLO).
• Identify and address technical gaps within the team, connecting delivery needs with the team’s skills development.
• Coordinate joint efforts with other tribes when solutions exceed the Platform team’s scope, sharing technologies that can be reused.
• Anticipate technical risks and dependencies during business discussions, influencing backlog refinement and prioritization of technical debt.
• Lead technical rituals and mentoring to raise the bar of seniority, turning complex challenges into learning opportunities for Mid-level and Senior engineers.

Staff Platform Engineer

• Operate and evolve the data platform infrastructure, ensuring scalability, security and high availability
• Automate resource provisioning with Terraform, maintaining quality standards
• Support the operation of the internal AI platform, ensuring necessary resources for applications such as LLM gateways, agents and interfaces
• Implement and monitor observability standards: ensure effective logging, alerts and metrics across all supported platforms
• Collaborate with Data, AI and Infrastructure technical teams, providing practical solutions for critical and high-availability environments
• Document technical decisions, processes, procedures and other operational best practices to facilitate team growth and stack evolution

Senior Data Platform Engineer

• This engineer will help build a corporate Internal Development Platform (IDP) for software and AI, used by multiple product engineering teams globally.
• Design and implement cloud infrastructure as code
• Build and maintain CI/CD pipelines
• Deploy containerized workloads (Kubernetes)
• Establish observability (logs, metrics, alerts)
• Create reusable “golden path” templates
• Enable secure and scalable deployment patterns
• Automate everything possible

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Platform Engineer

Lead AI Platform Engineer

Data Platform Engineer

Staff Platform Engineer

Senior Data Platform Engineer

AI Platform Engineer