Senior Software Engineer (AI-First, Full-Stack, AWS, Kubernetes)

Western Governors University•Salt Lake, UT

1d•Remote

About The Position

We are seeking a Senior Software Engineer who thrives in ambiguous problem spaces and excels at technical research, discovery, and analysis. This role requires strong end‑to‑end engineering skills across frontend and backend systems, along with hands‑on experience designing and delivering AI‑powered applications using Large Language Models (LLMs) deployed on AWS and Kubernetes. In this role, you will lead technical discovery efforts, explore emerging approaches, and translate loosely defined problems into scalable, production‑ready solutions. You will work closely with product, design, and engineering partners to shape user‑facing experiences and cloud‑native, containerized platform capabilities. This is a hands‑on senior individual contributor role with strong ownership of architecture, delivery, and mentoring.

Requirements

6+ years of professional software engineering experience
Bachelor's Degree - equivalent relevant experience performing the essential functions of this job may substitute for education degree requirements
Strong proficiency in a modern backend language (e.g., Java, Kotlin, Python, or similar)
Strong frontend experience with Angular, or equivalent frameworks
Significant experience building and operating production systems on AWS
Hands‑on experience deploying and managing applications on Kubernetes
Proven ability to work in ambiguous problem spaces and drive clarity via research and experimentation
Experience building or integrating AI‑powered features using Large Language Models
Experience with Kubernetes fundamentals: pods, deployments, services, ingress, and autoscaling
Experience running production workloads on EKS or comparable Kubernetes platforms
Familiarity with container security, resource optimization, and operational best practices
Experience debugging and operating distributed systems in containerized environments
Experience with prompt design, evaluation, and iterative experimentation
Familiarity with RAG architectures, tool‑using agents, or workflow orchestration
Ability to reason critically about AI behavior rather than treating models as black boxes

Nice To Haves

Experience building SaaS or platform products using AWS and Kubernetes
Experience with service meshes, event‑driven architectures, or workflow orchestration
Prior experience mentoring engineers or leading cross‑team initiatives

Responsibilities

Lead research and discovery for complex, ambiguous problem spaces through architectural spikes, prototypes, and proof‑of‑concepts
Evaluate new technologies, AWS services, Kubernetes patterns, and AI capabilities
Perform build‑vs‑buy and managed‑service vs. self‑managed tradeoff analyses
Translate findings into clear architectural direction and implementation plans
Design, implement, and evaluate AI‑powered features using Large Language Models
Apply prompt engineering, retrieval‑augmented generation (RAG), and agent‑based workflows
Design AI systems that account for latency, cost, reliability, observability, and safety
Deploy AI services in containerized environments and integrate them with cloud‑native infrastructure
Stay current with applied AI/LLM advancements and production best practices
Own features end‑to‑end, from frontend experiences to backend services and data layers
Build modern, responsive user interfaces using Angular, or similar frameworks
Design and implement scalable backend services and APIs
Ensure solutions meet high standards for performance, reliability, security, and maintainability
Design, deploy, and operate cloud‑native systems on AWS using Kubernetes
Build and operate containerized workloads using Docker and Kubernetes (EKS or equivalent)
Design Kubernetes deployments, services, ingress, autoscaling, and resource management strategies
Implement secure, highly available architectures using AWS and Kubernetes best practices
Apply Infrastructure as Code using tools such as Terraform and Helm
Monitor, troubleshoot, and optimize distributed systems using cloud and Kubernetes observability tools
Design and maintain CI/CD pipelines that build, test, and deploy containerized applications
Support zero‑downtime deployments and safe rollout strategies (e.g., blue/green, canary)
Participate in on‑call rotations and incident response for production systems
Drive improvements in reliability, scalability, and developer experience
Provide technical leadership on projects and guide architectural decisions
Mentor engineers through design reviews, code reviews, and hands‑on collaboration
Partner with product managers and designers to align technical solutions with user needs
Communicate clearly about technical tradeoffs, risks, and system behavior