Apr 6, 2026 4 hr. ago

AI DevOps Engineer

Apply

Apr 6, 2026 4 hr. ago

Discussion

GuruSchools AI DevOps Engineer

Apply

Discussion

Job Title:- AI DevOps Engineer
Location:- Spring Texas (On-Site)
Job Type:- Long Term Contract

Overview

We are seeking a DevOps Engineer to support a high impact, multi?workstream AI program focused entirely on enabling the rapid, reliable, and secure delivery of AI/ML applications and services.
This role is 100% dedicated to AI initiatives, spanning early experimentation through full productionization.
You’ll build and operate the CI/CD pipelines, cloud infrastructure, and runtime platforms that power data?intensive and model?driven workloads.
Working closely with HPCSIT Product teams, Data Engineers, and Data Scientists, you’ll help streamline the full AI model lifecycle and ensure enterprise-grade reliability, security, and scalability.
This is an excellent opportunity for a DevOps professional eager to work at the intersection of cloud, automation, and AI delivery.

Top 3 Skills for Success

CI/CD Tooling Infrastructure as Code (Terraform – primary) Cloud Platforms (Azure & AWS)

Success Measures

Reduced lead time for changes and faster, more reliable model-to-production cycles. High service availability, scalability, and performance. Strong security posture and auditability across platforms, pipelines, and runtime. Positive feedback from Architects, Data Science, Product, and Security stakeholders.

Ways of Working

Core collaboration hours aligned to Houston / Central Time preferred. Operate within Agile teams and methodologies (Scrum, stand-ups, sprint planning). Participate in daily and weekly ceremonies and be available during critical delivery windows. Embrace a culture of transparency, accountability, and continuous improvement.

Preferred Qualifications

Experience operating AI/ML workloads, including:Model packaging Artifact registries Model versioning Controlled and staged rollouts Familiarity with ML platforms and toolchains (e.g., MLflow, Azure ML, or equivalent frameworks). Data engineering awareness, including batch and streaming pipelines, data quality, schema, and version management. GPU-aware deployments and cost/performance tuning for inference and training environments. Experience working in highly regulated or large enterprise environments.

Required Qualifications

3–5+ years of experience in DevOps or Platform Engineering, including production support. Strong experience with Windows Server administration and troubleshooting; Linux experience is a plus. Hands-on expertise with CI/CD tooling such as Azure DevOps, GitHub Actions, and Ansible. Solid experience with cloud platforms (Azure and AWS), including networking, identity, and storage. Deep proficiency with Infrastructure as Code, with Terraform as the primary tooling. Strong scripting and automation skills (PowerShell required; Python a plus). Hands-on experience with Docker and Kubernetes, including deployments, scaling, upgrades, and operational hardening. Experience implementing monitoring, observability, and alerting solutions (e.g., Datadog, Azure Monitor, App Insights). Strong understanding of cost governance, resource design, and platform architecture best practices. Proven implementation of security best practices across pipelines and runtime environments (e.g., JFrog, RBAC, secrets). Excellent communication skills with the ability to collaborate across product, data, security, and architecture teams.

Responsibilities

Platform & CI/CD Pipelines

Design, implement, and maintain CI/CD pipelines for applications, platforms, APIs, batch jobs, and data pipelines.
Automate model build, test, packaging, versioning, and deployment workflows across dev, test, and production environments.
Establish repeatable deployment patterns that support fast iteration and reliable promotion across environments.

Cloud & Infrastructure

Provision and manage cloud infrastructure (compute, networking, storage) optimized for AI workloads, including batch, containerized, and serverless runtimes.
Implement Infrastructure as Code (IaC) using Terraform (primary) and/or Bicep.
Design and support environment promotion strategies aligned with enterprise standards.

Containers & Orchestration

Build, deploy, and optimize containerized services using Docker.
Operate workloads on managed container platforms such as OpenShift and AKS.
Implement autoscaling, resiliency, and operational hardening best practices.

Observability & Reliability

Establish and maintain monitoring, logging, and tracing across applications, models, and data pipelines.
Proactively troubleshoot platform, pipeline, and runtime issues using tools such as Datadog, Azure Monitor, and Application Insights.
Drive high availability and performance across AI services.

Security, Compliance & Governance

Embed security controls into CI/CD pipelines, including automated scanning, CodeQL, secrets management, and vulnerability detection.
Implement RBAC, secrets management, and data protection controls aligned with enterprise security and compliance standards.
Support auditability across code, pipelines, and runtime environments.

Collaboration & Enablement

Partner with Data Scientists and ML Engineers to streamline the AI/ML lifecycle from experimentation to production.
Create and maintain documentation, runbooks, reusable templates, and reference architectures.
Enable teams by sharing best practices for DevOps, platform usage, and AI delivery.

Apply

Description

Skills

See also