Python Go Developer Jobs in Huntington Beach, CA

Location: West Hollywood / Los Angeles, CA
Work Model: On-site (5 days per week)
Employment Type: Full-Time
Compensation: $200,000–$300,000+ USD (depending on experience and seniority), plus a competitive sign-on bonus.

Applicants must be legally authorized to work in the United States. Visa sponsorship is not available for this role.

Our client is a well-funded, early-stage AI company building a next-generation intelligence platform for high-stakes, real-world decision making.

The platform ingests and fuses data from satellite feeds, autonomous sensors, logistics networks, enterprise systems, and open-source intelligence (OSINT) to power production AI/ML workloads, knowledge graphs, and intelligent decision-making systems.

This is not a traditional SaaS, DevOps, or chatbot company. The engineering team is building production AI infrastructure where reliability, scalability, security, and developer productivity are mission-critical.

We're looking for a Senior, Lead, or Principal Platform Engineer who enjoys building platforms—not simply maintaining them. You'll own the cloud infrastructure, Kubernetes platform, CI/CD and GitOps workflows, infrastructure automation, and internal developer platform that enables engineering teams to build and deploy production AI systems at scale.

This is a highly collaborative, hands-on engineering role with significant ownership and influence over the platform architecture.

As a Platform Engineer, you'll design, build, and operate the infrastructure that powers complex AI/ML workloads, while creating the internal tooling and platform capabilities that help software engineers move faster and more reliably.

The ideal candidate has a strong software engineering foundation, deep cloud infrastructure expertise, and experience owning production Kubernetes environments from design through day-to-day operations.

• Design, build, and operate scalable cloud infrastructure supporting production AI/ML workloads.
• Own Kubernetes infrastructure, including architecture, networking, security, upgrades, scaling, and operational reliability.
• Build and evolve an internal developer platform that improves engineering productivity and deployment velocity.
• Develop self-service infrastructure and automation that enables engineering teams to ship software quickly and safely.
• Continuously improve developer experience through platform engineering best practices.

• Design and implement modern CI/CD and GitOps workflows for production environments.
• Build reusable Infrastructure-as-Code solutions using Terraform and related tooling.
• Architect highly available, resilient, and cost-efficient cloud infrastructure.
• Drive adoption of containerization, Kubernetes, and cloud-native infrastructure across engineering teams.
• Support AI-powered development workflows using tools such as Claude Code, Cursor, GitHub Copilot, or similar technologies.

• Build and optimize infrastructure supporting GPU-accelerated machine learning workloads.
• Improve GPU provisioning, scheduling, utilization, and resource management.
• Support scalable infrastructure for model training, inference, and AI services deployed in production.
• Partner closely with AI engineers to optimize platform performance and reliability.

• Lead the investigation and resolution of complex production incidents across cloud infrastructure, Kubernetes, networking, and applications.
• Perform root-cause analysis and implement long-term improvements that increase reliability.
• Build comprehensive monitoring, alerting, logging, and observability solutions.
• Drive platform reliability, performance optimization, and operational excellence.

• Partner with software engineers, AI engineers, security teams, and technical leadership on platform architecture decisions.
• Produce technical design documentation for major infrastructure initiatives.
• Champion engineering best practices around automation, scalability, security, testing, and reliability.
• Evaluate emerging technologies that improve infrastructure capabilities and developer productivity.

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related technical discipline (Master's preferred).
• 5+ years of experience building and operating production cloud infrastructure, Platform Engineering, DevOps, or Site Reliability Engineering (SRE) environments.
• Strong software engineering foundation with experience building automation, tooling, services, or developer platforms using Python, Go, Bash, or similar languages.
• Demonstrated ownership of production Kubernetes clusters, including architecture, networking, upgrades, scaling, and operational support.
• Hands-on experience designing and building Infrastructure-as-Code solutions using Terraform, including authoring reusable modules.
• Strong experience designing and building CI/CD and GitOps pipelines—not simply maintaining existing pipelines.
• Deep experience with Google Cloud Platform (GCP) and/or AWS.
• Strong understanding of containerization technologies including Docker and Kubernetes.
• Experience building and operating production-scale distributed systems.
• Strong troubleshooting skills across cloud infrastructure, Kubernetes, networking, and applications.
• Experience with observability platforms such as Prometheus, Grafana, Datadog, ELK, or equivalent.
• Excellent communication and collaboration skills.

Experience with one or more of the following is highly desirable:

• AI/ML infrastructure and GPU-accelerated workloads.
• NVIDIA GPU infrastructure and CUDA environments.
• Internal developer platforms and self-service infrastructure.
• GitOps methodologies.
• AI-native development tools such as Claude Code, Cursor, GitHub Copilot, or Codex.
• Security-focused environments including DevSecOps practices.
• Air-gapped, sovereign, or highly regulated deployment environments.
• Defense, aerospace, government, or other mission-critical industries.
• FedRAMP, ITAR, CMMC, or similar compliance frameworks.
• Serverless architectures and distributed systems.

Successful candidates will demonstrate:

• A platform engineering mindset with experience designing, building, and owning infrastructure—not simply maintaining existing environments.
• A strong software engineering foundation and passion for automation.
• Experience building platforms and internal tooling that improve developer productivity.
• Excellent systems thinking across cloud infrastructure, Kubernetes, networking, security, and distributed systems.
• A high level of ownership and comfort working in fast-moving environments with significant technical responsibility.
• A pragmatic approach to balancing reliability, scalability, security, and developer experience.

• Base salary: $200,000–$300,000+, depending on experience and seniority.
• Competitive sign-on bonus.
• Comprehensive benefits package.
• Opportunity to join a well-funded, high-growth AI company at an early stage with significant technical ownership.
• Long-term career growth with opportunities to take on broader platform and infrastructure leadership responsibilities as the organization continues to scale.

• Build production infrastructure powering real-world AI systems—not internal IT or traditional enterprise DevOps.
• Own the Kubernetes platform, developer experience, and cloud infrastructure that enables AI engineers to move faster.
• Work alongside a highly technical engineering team solving challenging platform and infrastructure problems.
• Support GPU-accelerated AI/ML workloads deployed in production.
• Help shape the technical foundation of a rapidly growing AI company where engineering quality, ownership, and innovation are highly valued.

If you're passionate about Platform Engineering, cloud infrastructure, Kubernetes, automation, and building the systems that power next-generation AI applications, we'd love to hear from you.