AI DevOps Engineer

Covalent Resource Group; Southfield (MI); On-site; USD 130,000 - 170,000; Full time

Job summary

Covalent Resource Group seeks an AI DevOps Engineer to own deployment, reliability, and telemetry for an agentic platform in the United States. You will design and operate CI/CD pipelines, cloud infrastructure, and release machinery to keep systems healthy and observable.

You will implement structured traces, cost-aware infrastructure, SLOs, and blameless postmortems, helping teams ship AI safely at scale while maintaining strong incident response and on-call readiness.

Qualifications

• 6-8 years in DevOps, SRE, platform, or infrastructure engineering.
• Deep CI/CD experience with pipelines that gate, test, and release production software.
• Strong AWS cloud operations and infrastructure-as-code experience.
• Hands-on observability with metrics, logging, tracing, dashboards, and alerts.
• SRE fundamentals: SLOs, incident response, on-call, capacity planning.

Responsibilities

• Own CI/CD for the platform and establish safe default patterns.
• Manage cloud infrastructure as code on AWS, with cost controls.
• Run the reliability practice including SLOs and incident response.
• Build agent telemetry and observability layer with structured traces and dashboards.
• Set operational standard by example for deployment and observability.

Skills

CI/CD

Cloud operations

Observability

SRE fundamentals

Incident response

Cost management

Tools

GitHub Actions

GitLab CI

Terraform

CDK

OpenTelemetry

Prometheus

Grafana

Datadog

CloudWatch

LangSmith

Langfuse

Arize

Databricks

Docker

Kubernetes

Job description

We are looking for an AI DevOps Engineer to own how our client's AI systems get deployed, stay up, and stay observable. You will own the infrastructure and operational substrate for our client's agentic platform - the CI/CD pipelines, the cloud environments, the release machinery, the reliability practice, and the telemetry layer that turns opaque agent behavior into something you can measure, alert on, and debug.

Our client's engineers build the AI agents; you make shipping them safe, repeatable, and observable. Right now, most of their apps have no CI/CD, the infrastructure is fragmented, and the agentic systems being stood up have little more than print statements for observability. Building that operational foundation is the job.

Agentic systems fail differently from ordinary services - nondeterministic output, silent quality drift, runaway tool call loops, and cost that spikes without warning. Standard DevOps is necessary but not sufficient. This role exists because someone has to own reliability and telemetry for systems that don't fail the way the runbooks assume.

KEY JOB RESPONSIBILITIES:

• Own CI/CD for the platform. Build the pipelines that take AI systems from commit to production - automated testing, evaluation gates, security and dependency checks, controlled and canary releases, and one-command rollback. Most of their apps have no pipeline today; you will establish the pattern and make the safe path the default path.
• Manage cloud infrastructure as code. Own the cloud environments (AWS primarily) the platform runs on - provisioning, networking, secrets, environment parity, and cost controls - as versioned, reviewable infrastructure-as-code, not hand-tuned consoles. You are accountable for environments that are reproducible, least-privilege by default, and cheap to stand up and tear down.
• Run the reliability practice. Own production reliability: SLOs, on-call and incident response, capacity and cost management, self-healing loops that detect and recover from failures, and blameless post-incident review. You will help define what "up" and "healthy" even mean for a nondeterministic system.
• Build the agent telemetry and observability layer. Instrument the platform so agent behavior is legible: structured traces of agent runs and tool calls, token and cost accounting, latency and success metrics, output-quality tracking over time, and the dashboards and alerts that surface a regression before a user does. When an agent misbehaves in production, the telemetry you built is how the team finds out and figures out why.
• Set the operational standard by example. On a small, high-leverage team, your pipelines and dashboards are the template. You establish the deployment patterns others adopt, the observability every new system gets wired into by default, and the operational discipline that lets a lean team run production systems well.

REQUIRED KNOWLEDGE, EDUCATION, EXPERIENCE, SKILLS, AND ABILITIES:

• 6-8 years in DevOps, SRE, platform, or infrastructure engineering, with a track record of running production systems you were accountable for
• Deep CI/CD experience - you have built and owned pipelines (GitHub Actions, GitLab CI, or similar) that gate, test, and safely release real production software
• Strong cloud operations, ideally AWS - provisioning, networking, secrets, and cost management as infrastructure-as-code (Terraform, CDK, or similar)
• Hands-on observability experience - metrics, logging, distributed tracing, dashboards, and alerting (OpenTelemetry, Prometheus/Grafana, Datadog, CloudWatch, or similar) - and the instinct to instrument first
• SRE fundamentals: SLOs, incident response, on-call, capacity planning, and blameless postmortems

NICE TO HAVE:

• Experience operating AI or LLM systems in production - token and cost accounting, prompt and evaluation-score tracking, or LLM observability tooling (LangSmith, Langfuse, Arize, or similar)
• Familiarity with the failure modes of nondeterministic systems: quality drift, runaway loops, cost spikes, non-reproducible output
• Experience with Databricks or a similar lakehouse platform, and with tool-integration layers such as MCP
• Container and orchestration experience (Docker, Kubernetes, or serverless equivalents)
• Experience in a non-software-company engineering organization - internal tools, corporate IT transformation, or similar Experience standing up an internal platform or golden-path deployment pattern that other teams adopted

WHY THIS ROLE:

You will build the operational foundation an entire organization's AI runs on - the pipelines, the