Principal AI Infrastructure Engineer, VideoAmp

• 7+ years of software engineering experience, with 1+ years in AI/ML infrastructure, LLM platform engineering, or agentic system development
• Strong Go (Golang) engineering skills; backend systems are Go-first. Proficiency in Python and SQL is also expected
• Production API design and operations: strong background in resource-based API design and experience building or consuming developer-facing platform APIs at scale, including on-call and incident response
• Multi-tenant system design: experience implementing rate limiting, caller attribution, tenant isolation, and external support surfaces for shared production APIs
• Deep experience with LLM APIs such as Anthropic or OpenAI, including prompt engineering and tool use/function calling
• Practical experience designing agent loops for production systems, including context and state management across turns, prefix caching for latency and cost, and coordinating multi-step task execution
• Experience with MCP or equivalent tool-layer abstractions for exposing platform capabilities to AI agents, or for building agents that consume them
• Familiarity with A2A protocols and long-running, durable workflows
• Experience building automated test and evaluation suites for LLM-based systems, including golden scenarios, regression detection, and quality gates in CI/CD
• Track record of working across engineering, product, and operations teams to drive alignment and rollout of shared infrastructure and capabilities
• Ability to produce design documents, postmortems, and system diagrams that create shared understanding across technical and non-technical stakeholders
• Fine-tuning experience such as LoRA or PEFT

Desirable

• The Principal AI Infrastructure Engineer will serve as a technical cornerstone of VideoAmp's AI Infra team, driving the design and execution of agentic workflow systems that bridge VideoAmp's platform APIs and AI-powered customer experiences
• This is a high-impact individual contributor role at the intersection of LLM infrastructure, API design, production reliability, and developer enablement
• You will architect and own production-critical systems serving live customers, operate in a rigorous evaluation-driven culture, and help VideoAmp safely scale its agentic platform to direct enterprise consumers
• The AI Infrastructure team owns the production-critical stack that powers VideoAmp's agentic experiences. At the core, agent runtimes leverage MCP tools and specialized agents to deliver end to end experiences and workflows
• A comprehensive observability layer acts as the basis for replay, analysis, and supervised learning. Quality is ensured via an evaluation framework that provides automated workflow testing with quality gates wired into release pipelines
• The team is actively building the next phases of the platform, including A2A APIs, multi-agent delegation, durable long-running agent task runtimes, reusable skills, workflows, agent memory, and agent knowledge
• You will have the opportunity to shape how these systems scale, how they interoperate, and how the next generation of VideoAmp's agentic platform is designed from the ground up
• Own multi-tenant tool layers. Design and implement multi-tenant isolation, rate limiting, and caller-attribution systems as direct enterprise customers use MCP tools
• Extend A2A interfaces and experiences. Enable customer and internal agents to invoke other agents in support of new initiatives
• Build and own durable agent runtimes, including long-running execution, task lifecycle management, and failure recovery
• Lead evaluation-driven development. Design golden scenario suites, automated CI/CD evaluation pipelines, and regression detection across all workflows. Evaluation is a parallel engineering workstream, not an afterthought
• Implement progressive discovery strategies for context, tools, agents, and skills, using deferred loading, search, categorization, and semantic filtering
• Design the agent harness and orchestration loop, leveraging isolated context, shared memory, and multi-agent coordination
• Drive new agentic capabilities from design to launch for customer experiences, owning production rollouts
• Participate in on-call rotation for customer-facing production systems; contribute to incident response, postmortems, and reliability improvements. This team practices full-lifecycle ownership
• Partner with internal engineering teams to negotiate and promote API-first designs that serve both programmatic and agentic consumers
• Contribute to multi-provider LLM abstraction layers, ensuring flexibility among LLM providers
• Facilitate AI office hours and cross-team enablement, serving as the internal expert on LLM tooling, agentic patterns, and tool configuration for teams across VideoAmp engineering
• Author, review, and drive clear technical requirements documentation for new solutions: design docs, architecture diagrams, and postmortems