Full-Stack ML Infra Engineer – DevOps & MLOps

Pramaana; Palo Alto (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Pramaana Labs in Palo Alto is seeking a full-stack engineer focused on infrastructure to accelerate our AI verification platform. You will improve DevOps and MLOps workflows, build repeatable development environments, and create self-serve tools for researchers and engineers.

You’ll work across interfaces, back-end services, and infrastructure automation, shaping identity, access, monitoring, and cost visibility as needs evolve. A strong product mindset and collaborative approach are essential.

Qualifications

• 2–3+ years of software engineering experience across backend, full-stack, or internal tools.
• Experience building services and automation in Python or TypeScript.
• Experience with cloud deployment (AWS) and CI/CD.
• Familiarity with AI/ML workflows and model deployment is a plus.
• Product mindset for internal users with clear interfaces and documentation.
• Strong collaboration and code quality practices.

Responsibilities

• Improve build and deployment processes with CI/CD, automated tests, and reproducible environments.
• Build tools for AI development lifecycle, versioning datasets/models, and job reproducibility.
• Create full-stack, self-serve applications and APIs for launching jobs and managing configs.
• Make systems observable and understandable by connecting logs, metrics, traces, and usage data.
• Contribute to shared platform services like identity, access controls, API keys, and budgets.
• Evaluate open-source or managed tools, and build custom software when needed.
• Work with engineers and researchers to prioritize improvements and measure impact.

Skills

Python

TypeScript

React

APIs

Databases

AWS

CI/CD

Deployment automation

Tools

Terraform

Kubernetes

OpenTelemetry

MLflow

Langfuse

Temporal

Job description

Pramaana Labs in Palo Alto is seeking a full-stack engineer focused on infrastructure to accelerate our AI verification platform. You will improve DevOps and MLOps workflows, build repeatable development environments, and create self-serve tools for researchers and engineers.

You’ll work across interfaces, back-end services, and infrastructure automation, shaping identity, access, monitoring, and cost visibility as needs evolve. A strong product mindset and collaborative approach are essential.