Chief Data Architect

Há 7 dias

Brasil beBeeCloudNative Tempo inteiro US$180.000 - US$210.000

AI Infrastructure Engineer Job Description

Solvd is an AI-first advisory and digital engineering firm delivering measurable business impact through strategic digital transformation. Our mission is to empower passionate people to thrive in the era of AI while maintaining rigorous ethical AI standards.

About the Role:

This position involves designing, implementing, and maintaining cloud-native infrastructure to support AI and data workloads with a focus on AI and data platforms. It also involves building and managing scalable data pipelines to ingest, transform, and serve data for machine learning and analytics purposes.

  • Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments.
  • Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production.
  • Drive best practices for observability including monitoring, alerting, and logging for AI platforms.
  • Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types.
  • Stay current with new tools and technologies to recommend improvements to architecture and operations.
  • Integrate AI models and large language models into production systems to enable use cases using architectures like retrieval-augmented generation.
Required Skills and Qualifications:
  • A minimum of 7 years of professional experience in software engineering and infrastructure engineering.
  • Extensive experience building and maintaining AI/ML infrastructure in production, including model deployment and lifecycle management.
  • Strong knowledge of AWS and infrastructure-as-code frameworks, ideally with CDK.
  • Expert-level coding skills in TypeScript and Python building robust APIs and backend services.
  • Production-level experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows.
  • Expert-level understanding of containerization (Docker), and hands-on experience with CI/CD pipelines, orchestration tools (e.g., ECS).
  • Proven ability to design reliable, secure, and scalable infrastructure for both real-time and batch ML workloads.
  • Ability to articulate ideas clearly, present findings persuasively, and build rapport with clients and team members.
  • Strong collaboration skills and the ability to partner effectively with cross-functional teams.

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