Remote-Eligible Senior AI Platform Engineer

Kiewit (Canada)

Portland (OR)

Hybrid

USD 130,000 - 150,000

Full time

4 days ago

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Benefits offered by this job

Medical, Dental & Vision plans

Wellness programs (voluntary)

Employee assistance programs

Life Insurance

Disability insurance

Retirement plans with matching

Generous paid time off

Job summary

Kiewit Infrastructure Engineers is seeking a Senior Software Engineer to build an enterprise AI platform. You will own features across AI, backend, data, and UI components and work with Kubernetes workloads in production.

The role involves collaborating with security, data owners, and platform teams, delivering high-quality, maintainable software that meets business needs. You will deploy and troubleshoot containerized workloads, evaluate AI workflows with domain experts, and ensure secure data

Qualifications

5+ years of professional software engineering experience.
Bachelor’s degree in CS/Engineering or equivalent.
Hands-on Kubernetes experience deploying and troubleshooting.
Strong Python or TypeScript with React.
Experience integrating LLMs or agent workflows.
Experience using AI-assisted development tools.
Solid fundamentals across API design, data modeling, distributed systems, auth, testing, CI/CD.
Ability to move prototypes to production with security and operability.
Clear communication across teams.

Responsibilities

Own features and services through production. Clarify requirements and acceptance criteria, design and implement changes across the relevant AI, backend, data, and UI components, and support their operation after release.
Build and evaluate AI workflows. Integrate language models, retrieval, and enterprise tools. Work with domain experts to define task success and use representative evaluations to assess quality, failure behavior, latency, and cost.
Help domain experts deliver their ideas. Participate early in design, pair on implementation, and guide prototypes through architecture review, hardening, integration, and delivery. Explain tradeoffs clearly and help contributors develop stronger software practices.
Work directly with Kubernetes. Deploy, troubleshoot, and improve containerized application workloads, including configuration, networking, storage, resource usage, observability, and rollout and recovery behavior. Coordinate with platform teams on shared infrastructure.
Review and improve implementation quality. Review human-written and AI-generated code for correctness, security, reliability, and maintainability. Trace changes across components, challenge assumptions in the code and its tests, and address underlying causes of defects.
Protect company data and user access. Build integrations that respect user permissions, preserve data integrity, and handle AI-driven actions and failures safely, in partnership with security and data owners.
Strengthen software engineering practices. Improve existing workflows through meaningful automated tests, code review, error handling, CI/CD, documentation, and operational visibility. Keep solutions understandable and proportionate to the problem.
Make capabilities clear and usable. Carry features through UI integration and documentation, and keep ownership, dependencies, readiness, rollout status, and known limitations visible to the team.