Senior AWS DevOps Engineer, Data and AI Platforms

About the role

Location: Houston, TX (Downtown), fully onsite 5 days per week, local candidates only. Duration: 6-Month Contract (W2).

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.

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