Senior AI Infrastructure Engineer, TetraScience

• 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) is a plus
• 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

Desirable

• We’re looking for a Senior AI Infrastructure Engineer to help design, build, and scale our AI and data infrastructure
• In this role, you’ll focus on architecting and maintaining cloud-based MLOps pipelines to enable scalable, reliable, and production-grade AI/ML workflows, working closely with AI engineers, data engineers, and platform teams
• Your expertise in building and operating modern cloud-native infrastructure will help enable world-class AI capabilities across the organization
• If you are passionate about building robust AI infrastructure, enabling rapid experimentation, and supporting production-scale AI workloads, we’d love to talk to you
• Design, implement, and maintain cloud-native infrastructure to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock
• Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics
• 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 (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG)