Location:
Houston, TX (Downtown), fully onsite 5 days per week, local candidates only
Duration:
6-Month Contract (W2)
Schedule:
Standard Business Hours, 40 hours per week
Pay Rate:$99.00 - $103.00/hr
Position Summary
Our client, a leading midstream energy company headquartered in Houston, TX, seeks a Senior (Level III) DevOps Engineer to design, build, and run the DevOps capability for its AI and Data Engineering teams on AWS. Working directly with internal customers, this individual will design and document the overall DevOps solution architecture, then build the CI/CD pipelines, automation, and deployment paths that Data, ML, and AI Engineers use to ship data products, ML models, and GenAI applications. The ideal candidate knows the data engineering space well (how Snowflake, dbt, and Airflow workloads are built, tested, promoted, and operated) and understands what it takes to take an ML or GenAI service to production.
Working with minimal supervision alongside Security, Compliance, Data Engineering, AI Engineering, and the wider DevOps team, the engineer will establish reusable, templated practices with required gates and governance built in, making the compliant path the easiest path for developers. This role suits a daily, proficient user of AI-assisted engineering tools who will help shape how the team uses AI. All work is documented and handed over so the client's DevOps staff can operate and extend it.
Essential Functions and Responsibilities
- Initially, help automate Security and Compliance processes into the CI/CD pipelines and the client's compliance tool using Terraform, Python, and Shell
- Build the first reusable, templated CI/CD paths for data and AI workloads, with the required gates and evidence included
- Take over DevOps work currently handled within the Data Engineering team
- Establish baseline observability and the first runbooks for the data and AI platform, and identify the highest-toil processes to automate first
- Collaborate with internal customers (Data, ML and AI Engineering teams and their stakeholders) to understand their needs, then design and document the overall DevOps solution architecture: delivery paths, environments, gates, tooling and operating model; record design decisions and their rationale so others can extend the design
- Own CI/CD and deployment automation for data and AI workloads: Snowflake and dbt change promotion, Airflow/Astronomer DAG deployment, and ML model and GenAI service release, across AWS, containerized clusters and on-premises environments
- Partner with the Infrastructure Platform Engineering team on Infrastructure as Code (AWS CloudFormation, Terraform/OpenTofu, CDK) for multi-account AWS environments, and incorporate it into pipelines for data and AI platform services (S3, Glue, Athena, Lake Formation, SageMaker, Bedrock, Lambda, ECS, RDS, IAM, VPC)
- Automate CloudFormation stack/change set and Terraform plan/apply deployments, testing, and evidence
- Support MLOps practices, including model registries, pipeline automation, promotion across environments, and cost and guardrail controls for LLM-based systems
- Build reusable, versioned, templated pipelines and workflows that teams adopt rather than copy, with security and compliance controls built in by default
- Implement and automate required gates (approvals, segregation of duties, policy-as-code, scanning, change-ticket linkage) and produce auditable deployment evidence, working directly with Security and Compliance
- Make developer experience a first-class goal through self-service tooling, fast feedback, clear errors and good documentation, so the governed route is also the easiest; measure and improve it
- Build observability (metrics, logs, traces, dashboards, actionable alerting) and apply site reliability practices (SLIs/SLOs, capacity and cost review, disaster recovery testing, incident response and blameless post-incident reviews)
- Write and maintain tested runbooks (daily operations, periodic maintenance, failure modes, recovery) that other team members can use, and turn them into automation wherever possible
- Continuously reduce DevOps overhead by finding manual, repetitive, and high-volume work, then automating, simplifying, or eliminating it
- Use AI-assisted development tools daily to design, build, test, and refactor automation, and contribute to standards for AI-assisted DevOps, including critical human review of generated output
- Maintain collaborative working relationships with Data Development Engineers, Infrastructure Platform Engineers, and Security and Compliance team members, and mentor mid-level and junior engineers
- Meet with executives on assigned projects and work with both technical and non-technical stakeholders
- Occasional travel to West Houston
Education/Experience
- 10+ years in DevOps, platform, infrastructure, or site reliability engineering, including experience leading or mentoring other engineers
- 5+ years in the AWS ecosystem as a DevOps or platform engineer, with deep AWS expertise
- 5+ years with Infrastructure as Code on AWS, including AWS CloudFormation and Terraform or OpenTofu
- 5+ years scripting/programming in Python (building and testing tooling) and Bash/Shell on Linux/Unix
- 5+ years supporting CI/CD tools (GitHub Actions, Harness.io, ArgoCD, Jenkins, etc.), including reusable/templated workflow design, on GitHub, GitLab, or ADO repositories
- 5+ years supporting Kubernetes and containerized orchestration
- Experience delivering CI/CD for data platforms (Snowflake, dbt, Airflow) and working knowledge of MLOps for classical ML and/or GenAI systems
- Experience in change-controlled or regulated environments (approvals, segregation of duties, policy-as-code, auditable evidence), partnering with Security and Compliance teams
- Experience designing and documenting DevOps or platform solution architectures with customers or internal teams, including architecture diagrams and design decision records
- A record of writing runbooks that others used successfully, automating away manual operational work, and building tooling or templates that other engineers chose to use; candidates should be ready to show examples
- AWS architecture certification preferred
Knowledge/Skills
- Deep understanding of AWS cloud networking, security, and architecture patterns
- Proficiency with AI-assisted development tools such as Claude Code, Cursor, or GitHub Copilot, including structuring work for AI coding agents (clear specifications, context, task breakdown) and reviewing generated output critically
- Site reliability and observability skills, including SLIs/SLOs, monitoring and alerting platforms (Grafana, Prometheus, CloudWatch, Datadog or Elastic) and incident management
- Strong troubleshooting and incident-response skills
- Strong communication skills with both technical and non-technical stakeholders, up to executive level
- Open-minded, collaborative, and adaptive, with an eagerness to learn
- Preferred: experience with S3 Tables, Glue, Lake Formation, SageMaker and Bedrock
- Preferred: experience with Harness.io, including templated pipelines, governance policies (OPA) and approval/evidence features
- Preferred: experience with Astronomer/Airflow operation and upgrades, and with change-data-capture pipelines
- Preferred: experience with LLM observability, evaluation and cost controls, and with AI agents or MCP-style integrations in engineering workflows
- Preferred: experience with OIDC/workload identity federation and enterprise secrets/credential management
- Preferred: experience building internal developer platforms or golden-path tooling
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