Lead DevOps / AWS Architect

We’re working with a major oil / gas company in Downtown Houston, fully onsite 5x a week but you’ll have a chance to make a real impact, be a difference maker with a Fortune 100 company. We know it’s annoying an inconvenient for the people that still work hybrid or fully remote, but if you’re looking for a chance to level up your skill set rolling out “best practices” and establishing AI based solutions in data engineering and data science teams….this is the one.

JOB DESCRIPTION

We are looking for a Senior (Level III) DevOps Engineer to design, build and run the DevOps capability for our AI and Data Engineering teams on AWS. You will collaborate directly with our customers to design and document the overall DevOps solution architecture, then create the CI/CD pipelines, automation and deployment paths that Data Engineers, ML Engineers and AI Engineers use to ship data products, ML models and GenAI applications, so those engineers can stay focused on data and AI.

You will know the data engineering space well: how Snowflake, dbt and Airflow workloads are built, tested, promoted and operated, and what it takes to take an ML or GenAI service to production. You will work under minimal supervision alongside Security, Compliance, Data Engineering, AI Engineering and the wider DevOps team to establish reusable, templated practices with the required gates and governance built in, and to make the compliant path the easiest path for developers.

You will be a daily, proficient user of AI-assisted engineering tools and will help shape how our DevOps team uses AI well. Everything you build is documented and handed over so that Enterprise DevOps staff can operate and extend it.

Design, build and automate

• Collaborate with 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 reasons 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.
• Work with the Infrastructure Platform Engineering team on Infrastructure as Code (AWS CloudFormation, Terraform/OpenTofu, CDK) for our multi-account AWS environments, and incorporate it into our 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: 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.

Govern and enable

• 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: self-service tooling, fast feedback, clear errors and good documentation, so the governed route is also the easiest. Measure it and improve it.

Operate and improve

• 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: find manual, repetitive and high-volume work, then automate, simplify or eliminate it.

Work with AI, work with people

• Use AI-assisted development tools daily to design, build, test and refactor automation, and contribute to our 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/junior engineers.

REQUIREMENTS

• 10+ years in DevOps, pl…