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Contract Ai Infrastructure Engineer Jobs in Indiana

AI DevOps. Engineer

Indianapolis, IN Β· On-site

$50.50 - $69/hr

Git‑driven CI/CD, infrastructure as code, release and config management, observability, and the ... Partner with the AI Dev engineering team on platform standards; write runbooks, release guides, and ...

AI Engineer

Indianapolis, IN Β· On-site

$50K - $112K/yr

... and infrastructure to support reliable AI operations - Engaging in continuous learning to adapt to new technologies and methodologies in AI engineering What You Must Have - At least a Bachelor ...

... infrastructure, and AI agents into a unified platform. * Design scalable data and context ... Define and govern data contracts, integration standards, and observability practices to ensure ...

Quality Assurance Engineer

Brazil, IN Β· On-site

$100 - $125/hr

About Nectir Nectir is the secure AI infrastructure purpose-built for schools. Nectir AI provides ... We're hiring an engineer who can build the automation that turns a ~60-minute manual QA pass into a ...

Build tooling and infrastructure to allow training, evaluation and analysis of AI models at scale * Work cross-functionally with other scientists, engineers and product teams to ship AI systems which ...

As a Manager in AI Security Engineering, you will play a critical role in securing the development ... Technology & Infrastructure * Applications * Relationship Management * Strategy & Communications

Showing results 41-60

Contract Ai Infrastructure Engineer information

What is the difference between Contract Ai Infrastructure Engineer vs Contract Cloud Infrastructure Engineer?

AspectContract Ai Infrastructure EngineerContract Cloud Infrastructure Engineer
Required CredentialsRelevant certifications in AI, cloud platforms, and infrastructureCertifications in cloud platforms, networking, and systems administration
Work EnvironmentAI development labs, data centers, cloud environmentsCloud service providers, enterprise data centers, remote cloud setups
Employer & Industry UsageTech companies, AI startups, research institutionsIT firms, cloud service providers, large enterprises
Common Search & Comparison IntentUnderstanding roles in AI infrastructure projectsComparing cloud infrastructure roles and responsibilities

The Contract Ai Infrastructure Engineer focuses on building and maintaining AI-specific infrastructure, including GPU clusters and AI frameworks, often within AI-focused companies. In contrast, the Contract Cloud Infrastructure Engineer specializes in managing cloud environments, networks, and systems for various enterprise needs. Both roles require cloud and infrastructure knowledge but differ in their focus areas and industry applications.

What are the most commonly searched types of Ai Infrastructure Engineer jobs in Indiana?

The most popular types of Ai Infrastructure Engineer jobs in Indiana are:

What are popular job titles related to Contract Ai Infrastructure Engineer jobs in Indiana?

For Contract Ai Infrastructure Engineer jobs in Indiana, the most frequently searched job titles are:

What job categories do people searching Contract Ai Infrastructure Engineer jobs in Indiana look for?

The top searched job categories for Contract Ai Infrastructure Engineer jobs in Indiana are:

What cities in Indiana are hiring for Contract Ai Infrastructure Engineer jobs?

Cities in Indiana with the most Contract Ai Infrastructure Engineer job openings:

Infographic showing various Contract Ai Infrastructure Engineer job openings in Indiana as of September 2026, with employment types broken down into 79% Full Time, 9% Part Time, 6% Temporary, and 6% Contract. Highlights an 85% In-person, and 15% Remote job distribution.

AI DevOps. Engineer

Indianapolis, IN β€’ On-site

$50.50 - $69/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 24 days ago


Key responsibilities

  • Design and build Git-based CI/CD pipelines to automate build, test, and deployment processes.

  • Manage infrastructure as code using Terraform, Helm, and GitOps tools to provision and operate Kubernetes clusters and client environments.

  • Develop and maintain observability and monitoring systems, including alerting, telemetry, and performance tracking across client deployments.


Job description

Join Expedient's AI CTRL product team as our AI DevOps Engineerβ€” a senior, hands‑on engineer who will build the framework that manages, configures and ships agentic workflows, tooling applications, and AI integrations to clients quickly, safely, and repeatably.

You’ll own the path from commit to production: Git‑driven CI/CD, infrastructure as code, release and config management, observability, and the LLMOps practices that keep model‑powered systems reliable and cost‑efficient. This is a build roleβ€”you won’t be maintaining someone else’s pipelines; you’ll be creating the framework the AI Dev team builds on.

What You’ll Do:
  • CI/CD Pipelines: Design and build Git‑based pipelines that automate build β†’ test β†’ deploy for Retool apps, agentic workflows, MCP servers, and data connectorsβ€”turning manual client deployments into repeatable, gated releases.
  • Infrastructure as Code: Make the platform reproducible. Use Terraform, Helm, and GitOps (ArgoCD/Flux) to provision and manage Kubernetes (Nutanix NKP) clusters and per‑client environments as code.
  • Configuration Management: Manage environment and deployment configuration as code across a growing fleet of client deploymentsβ€”eliminate config drift and one‑off manual changes.
  • Release Management: Own versioning, environment promotion, release gates, and clean rollback. Maintain versioned, deployable artifacts so any release can be reproduced or reverted.
  • Observability & Tracing: Build the monitoring backboneβ€”Elastic/ECK, APM, and telemetry distributed tracingβ€”with deployment health, SLOs/SLIs, and usage/cost instrumentation across all client deployments. Strengthen alerting so issues surface before clients feel them.
  • LLMOps Practices: Stand up prompt and configuration versioning, model/prompt evaluation pipelines, A/B testing of prompts and models, multi‑provider traffic routing and failover, and token/cost dashboardsβ€”the AI‑specific discipline that keeps model‑powered systems accurate, available, and affordable.
  • Change & Risk Management (incl. Compliance): Implement controlled‑change processesβ€”approvals, audit trails, and guardrailsβ€”with compliance‑as‑code for SOC 2 audit logging, secrets management (e.g., vaults/sealed‑secrets), and SSO/OIDC configuration.
  • Automation Marketplace: Build an internal library of vetted, reusable workflows, connectors, and IaC modules that accelerate client deliveryβ€”and graduate proven items into a client‑facing catalog aligned to the Agentic Workflow Engine (AWE).
  • Collaborate & Document: Partner with the AI Dev engineering team on platform standards; write runbooks, release guides, and architecture docs that let the framework scale beyond.
What We’re Looking For:
  • Experience: 3–5 years in DevOps, platform engineering, site reliability, or MLOps/LLMOps. Prior experience at a managed service provider, SaaS company, or enterprise technology team is a strong plus.
  • Git‑based CI/CD: Designing automated build/test/deploy pipelines from scratch.
  • Infrastructure as Code: Terraform and Helm; GitOps with ArgoCD or Flux.
  • Kubernetes: Operating and automating clusters (Nutanix NKP or equivalent); namespaces, workloads, container lifecycle.
  • Observability: Elastic/ECK, APM, OpenTelemetry tracing; defining alerts, SLOs/SLIs (Prometheus/Grafana experience transfers).
  • Scripting & data: Strong Python and Bash; SQL fundamentals.
  • Secrets & identity: Secrets management (Vault or equivalent), SSO/OIDC configuration (Entra ID, Okta, OneLogin).
  • Workflow orchestration: Argo Workflows, Airflow, or similar (a plus).
  • LLM APIs: Working familiarity with Anthropic Claude, OpenAI, and/or Google Geminiβ€”prompt construction, tool use/function calling, token management.
  • RAG & MCP awareness: Chunking, embedding, vector search, context‑window management; Model Context Protocol integrations (a plus).
  • Compliance exposure: SOC 2 audit logging and controls‑as‑code (a plus).
  • Builder mindset: Sees a manual process and automates it; ships the framework, not just the fix.
  • Automation‑first & reliability‑minded: Treats infrastructure, config, and compliance as code; thinks in SLOs, blast radius, and rollback.
  • Documentation instinct: Writes the runbook before calling something done; updates the guide when the process changes.
  • Risk‑aware: Balances deployment velocity with controlled change and auditability.
  • Self‑directed, strong ownership mentality, excellent communicator, thrives in a fast‑paced environment.
  • Education: Bachelor’s in Computer Science, Engineering, Information Systems, or related field (or equivalent practical experience).
Location & Compensation:

Indianapolis, Cleveland, or Pittsburgh. Hybrid work model. Regional travel may be required.

Salary for this position is directly related to your own experience, knowledge, and skills. Estimated range for this role is $120,000 to $150,000.

Working for Expedient

We prioritize ongoing education and continuous innovation to remain at the forefront of the information technology landscape. Our commitment to learning is reflected in our comprehensive employee training and tuition reimbursement programs, which are driven by our employees and funded by Expedient 100%.

For our full‑time employees we offer an exceptional benefits package including three weeks of paid time off annually that increases with tenure plus your birthday off and a health holiday to be used for preventive care. We offer parental leave, top‑tier medical, dental, and vision, disability and life insurance, at an affordable rate, wellness engagement opportunities, and a 401(k) with a generous match.

We also recognize the importance of a comfortable and convenient work environment. We offer a hybrid work model for many roles, paid parking and other perks.

Expedient is an equal opportunity employer. Qualified applicants will receive fair and equitable consideration for employment without regard to their race, color, religion, national origin, gender, protected veteran status, disability, or any other characteristic protected by law.

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