Jobs at Invisible Technologies

Build and deploy AI-powered solutions for client operational problems. Responsibilities include scoping ambiguous requirements, designing LLM and retrieval workflows, developing scalable software, working with messy data, iterating with clients and operators, and creating reusable deployment infrastructure. The role combines AI engineering, software development, and technical consulting, with customer-facing collaboration, on-call responsibilities, and 25–50% travel for client engagements.

Own a platform domain end to end, defining architecture, roadmap, reliability, security, and cost standards. Build scalable multi-tenant Kubernetes infrastructure, enable self-service deployments, advance agent-native engineering practices, and develop tooling for safe agent execution. Partner with engineering teams to translate infrastructure needs into reusable capabilities, mentor engineers, lead design decisions, and manage on-call and post-incident processes.

Build and operate the company’s platform infrastructure, including Kubernetes, infrastructure as code, CI/CD, identity, secrets, networking, observability, and developer tooling. Responsibilities include operating production services, supporting agent infrastructure, improving alerting, participating in incident response, and creating self-service capabilities. The role requires strong Kubernetes and cloud experience, Terraform, Helm, GitOps, Python, AI coding-agent expertise, sound engineering judgment, written communication, and comfort with ambiguity.

Work directly with clients to scope, prototype, and deploy AI-powered workflows. Build scalable systems using LLMs, embedding and retrieval systems, and backend infrastructure. Translate messy data and operational constraints into production-ready solutions, iterate with stakeholders, and maintain deployed systems (including on-call support).

Lead end-to-end AI solutions as a forward-deployed engineer: scope, design, and deploy ML/LLM systems; partner with clients and stakeholders; build infrastructure, data pipelines, and reusable platform components; mentor engineers; own projects from discovery through production and provide on-call support.

Lead high-impact AI solutions, overseeing architecture and deployment. Partner with stakeholders, coach engineers, and drive project success from inception to launch.