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Buildkite Jobs in Texas (NOW HIRING)

SRS Consulting Inc

Need GoLang Developer

Frisco, TX · On-site

Buildkite * GitLab CI * Ability to troubleshoot large-scale build environments. Dependency & Migration Engineering * Experience performing: * Package upgrades * Framework migrations * Dependency ...

SRS Consulting Inc

Need GoLang Developer

Frisco, TX · On-site

... o Buildkite o GitLab CI Ability to troubleshoot large-scale build environments. Dependency & Migration Engineering Experience performing: o Package upgrades o Framework migrations o Dependency ...

Trustwise

DevOps Engineer

Austin, TX · On-site

$100 - $130/hr

Hands‑on experience with at least one major CI system (GitHub Actions, GitLab CI, Jenkins, Buildkite, CircleCI, Bitbucket Pipelines, etc.) and an understanding of the build and cache layers ...

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Buildkite information

What is Buildkite and what does a Buildkite engineer do?

Buildkite is a continuous integration and continuous deployment (CI/CD) platform that helps software teams automate the building, testing, and deployment of their code. A Buildkite Engineer is responsible for configuring and optimizing Buildkite pipelines, integrating Buildkite with source control systems, and ensuring efficient, reliable automation for software delivery. They often collaborate with developers and DevOps teams to troubleshoot build issues, improve workflow efficiency, and maintain secure, scalable CI/CD processes.

What are the key skills and qualifications needed to thrive as a Buildkite engineer, and why are they important?

To thrive as a Buildkite Engineer, you need strong experience in CI/CD pipelines, scripting languages (like Bash, Python, or Ruby), and a background in software development or DevOps. Familiarity with Buildkite's platform, cloud infrastructure (such as AWS or GCP), and tools like Docker, Git, and Kubernetes is typically required. Excellent problem-solving, communication, and collaboration skills help you work effectively with development and operations teams. These abilities ensure efficient automation, smooth deployments, and robust software delivery processes.

What are some typical challenges faced by engineers working with Buildkite pipelines, and how can they be addressed?

Engineers working with Buildkite pipelines often encounter challenges related to pipeline configuration complexity, managing secrets securely, and optimizing build times. To address these, it's important to modularize pipeline steps for maintainability, use environment variables or secret management plugins for sensitive data, and leverage Buildkite's parallelism and agent scalability features to speed up builds. Collaborating closely with development and DevOps teams ensures that best practices are shared and pipelines remain efficient and secure.

What is the difference between Buildkite vs Jenkins?

AspectBuildkiteJenkins
Required CredentialsCloud account, API accessServer setup, Java knowledge
Work EnvironmentCloud-based, SaaS platformSelf-hosted or cloud, open-source
Industry UsageDevOps, CI/CD pipelinesDevOps, CI/CD, automation
Common Search/ComparisonYesYes

Buildkite and Jenkins are both popular CI/CD tools used to automate software testing and deployment. Buildkite offers a cloud-based, user-friendly platform with minimal setup, ideal for teams seeking quick deployment. Jenkins, on the other hand, is an open-source, self-hosted solution with extensive plugin support, suitable for organizations needing customizable pipelines. While Buildkite emphasizes ease of use and cloud integration, Jenkins provides more control and flexibility for complex workflows.

What are popular job titles related to Buildkite jobs in Texas?

For Buildkite jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Buildkite jobs in Texas look for?

The top searched job categories for Buildkite jobs in Texas are:

Buildkite jobs near you
Infographic showing various Buildkite job openings in Texas as of August 2026, with employment types broken down into 97% Full Time, and 3% Contract. Highlights an 54% Physical, 8% Hybrid, and 38% Remote job distribution.

AI Engineer (Austin, TX or Cupertino, CA)

Austin, TX • On-site

SANS
See more jobs at SANSexternal

Other

Posted 23 days ago

Job description

  • 5+ years of experience in ML engineering, MLOps, platform engineering, or SRE, including 2+ years working hands-on with LLMs or LLM-powered applications in production.
  • Demonstrated experience building evaluation systems for ML or LLM applications: test harnesses, benchmark datasets, automated scoring (including LLM-as-judge approaches), and regression detection.
  • Strong software engineering skills in Python (and ideally TypeScript), with a track record of building reliable, well-tested internal platforms and tooling.
  • Deep familiarity with CI/CD systems (e.g., GitHub Actions, GitLab CI, Jenkins, Buildkite) and experience embedding automated quality gates into deployment pipelines.
  • Experience with observability and monitoring stacks (e.g., OpenTelemetry, Datadog, Grafana/Prometheus) and, ideally, LLM-specific observability tools (e.g., LangSmith, Langfuse, Arize Phoenix, Braintrust, W&B Weave).
  • Proven ability to debug complex distributed systems under pressure, including production incident response, root-cause analysis, and blameless postmortems.
  • Excellent cross-functional communication: able to translate evaluation results into clear findings and recommendations for both engineers and non-technical stakeholders.
  • Comfort with ambiguity and a builder’s mindset: this role starts with a blank page and ends with the evaluation platform the whole organization relies on.
  • Experience with agentic frameworks and orchestration patterns (e.g., multi-agent systems, tool use, RAG pipelines) and their distinct failure modes.
  • Experience with prompt management, model routing, or fine-tuning workflows and evaluating changes across model versions and providers.
  • Background in statistics or experimentation (A/B testing, significance testing, sampling strategies for human review).
  • Design and build reusable AI agent skills, plugins and maintain internal marketplace infrastructure to extend and scale Data, AIML capabilities across the organization.
  • Expertise in causal inference and measurement strategy including causal graphs, ontologies, and knowledge graphs to drive rigorous, decision grade data analysis.
  • Experience operating in regulated or high-stakes domains where agent errors carry real business or customer impact.

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