DevOps Engineer (AI)

TL;DR

DevOps Engineer (AI): Scaling and hardening the infrastructure for a physical AI platform designed for energy field workers with an accent on data pipelines and ML training platforms. Focus on building observability, reliability foundations, and CI/CD guardrails for high-volume processing across AWS and Kubernetes.

Location: Hybrid in New York City (2 days per week in person)

Salary: $160,000 – $220,000 USD

Company

hirify.global develops physical AI and robotics software to modernize field work for energy companies, increasing productivity for engineers and crews.

What you will do

• Lead the improvement and scaling of infrastructure for data pipelines, ML training platforms, and web applications.
• Build observability and reliability foundations, including monitoring, alerting, and SLO/SLI definitions for Apache Airflow on Astronomer.
• Design and implement CI/CD guardrails for production changes and safe rollout mechanics for Astronomer deployments.
• Improve the reliability and operational visibility of machine learning inference operations and output correctness.
• Create operational tooling, runbooks, and engineering standards to automate toil and improve debugging at scale.
• Collaborate with data platform and engineering teams to manage complex data movement across AWS (S3, SQS, Lambda) and Kubernetes.

Requirements

• 7-10 years of experience in observability, systems/infrastructure engineering, SRE, or DevOps.
• Must be based in the New York City area to work from the Lower Manhattan office (hybrid).
• Strong hands-on experience with Infrastructure-as-Code (Terraform).
• Proficiency in container orchestration and debugging (Kubernetes and/or ECS).
• Deep Linux debugging skills and ability to investigate production issues using logs and metrics.
• Ability to reason about end-to-end architecture with product impact and failure handling in mind.

Nice to have

• Experience with Apache Airflow and/or Astronomer.
• Experience with AWS and DuploCloud.
• Background in geospatial, imagery, LiDAR, or point-cloud domains.
• ML Ops skills related to model deployment, inference reliability, and CI/CD for model artifacts.
• Experience working in early-stage, fast-moving startup environments.

Culture & Benefits

• Opportunity to be the first full-time DevOps Engineer in a fast-growing, mission-driven startup.
• Collaborate with experts from top institutions (Penn, Caltech, CMU) and leading tech companies (Palantir, Stripe).
• Work on high-impact technology reducing wildfire risk and accelerating storm recovery for major US utilities.
• Hybrid work model in midtown Manhattan.