Staff AI Platform Engineer, Invoca

• We're looking for a creative and driven AI Platform Engineer who is excited to tackle complex problems and build impactful solutions. You likely have:
• Staff-level Platform Engineering Depth: 7+ years of professional experience in Platform engineering in AI/ML disciplines, with a strong focus on building shared infrastructure, platform products, developer tooling, or foundational backend systems used by a large number of users
• AI platform or agentic systems experience: You have worked on the infrastructure and framework layers behind AI systems such as agent orchestration, model-serving abstractions, evaluation systems, retrieval infrastructure, prompt/context tooling, MCP/A2A stack, platform APIs for AI development, etc
• Developer-facing product mindset: You care deeply about developer experience. You know how to design systems, interfaces, and abstractions that engineers actually want to use
• Strong distributed systems and backend engineering skills: You are highly effective in Python and/or TypeScript and are comfortable designing resilient services, APIs, asynchronous systems, and distributed workflows
• API and contract design ability: You have designed robust, typed, well-structured interfaces such as REST, gRPC, SDKs, or platform contracts that support long-term extensibility and adoption
• AI Systems Thinking Ability: You haven't just used frameworks like LangChain, LangGraph or LlamaIndex; you understand how they work under the hood and know when to abstract them away. You have built frameworks using them to solve various AI concerns for developers
• Experience with model infrastructure and operational concerns: You understand latency, throughput, reliability, token/cost management, model-provider integration, and the realities of running AI systems in production
• Experience with observability and evaluation: You have built or contributed to tracing, quality measurement, automated evaluation, or other systems that help teams understand and improve AI system performance
• Platform standardization instincts: You know how to take repeated engineering problems and turn them into reusable golden paths, shared components, and organizational standards
• Infrastructure as Code: Familiarity with Kubernetes, Docker, and Terraform
• Web Technologies: Intermediate to Expert level proficiency with TypeScript/JavaScript, React, and other web technologies
• Educational Foundation: Bachelor's Degree in Computer Science, Engineering, or a related field (or equivalent practical experience). An advanced degree (Master's or Ph.D.) is a strong plus
• Experience with retrieval systems, vector databases, and context-management infrastructure
• Experience with multi-tenant platform design, governance, or enterprise-grade controls
• This role is a strong fit if you have built:
• Developer-facing AI platforms
• Reusable orchestration or workflow systems
• Evaluation and experimentation infrastructure
• Platform APIs and SDKs for internal and/or external product/engineering teams
• Model-serving, routing, or AI control-plane infrastructure
• Shared AI tooling with strong operational rigor

Desirable

• You’ll join a team where everyone, including you, is striving to constantly improve their knowledge of software development tools, practices, and processes
• We are an incredibly supportive team. We swarm when problems arise and give excellent feedback to help each other grow. Working on our close-knit, multi-functional teams is a chance to share and grow your knowledge of different domains from databases to front ends to telephony and everything in between
• We are passionate about many things: continuous improvement, working at a brisk but sustainable pace, writing resilient code, maintaining production reliability, paying down technical debt, hiring fantastic teammates; and we love to share these passions with each other
• We are hiring a Staff AI Platform Engineer to help build the core platform that powers AI development at Invoca
• This role is not focused on building isolated AI features or one-off model applications; this is a platform engineering role. You will design and build the reusable systems, abstractions, interfaces, and operating standards that enable product teams to develop, ship, and operate AI capabilities quickly and safely
• You will work at the intersection of developer experience, backend systems, AI infrastructure, and platform architecture
• Your job is to turn common AI development problems into durable platform capabilities: SDKs, APIs, gateways, orchestration primitives, evaluation frameworks, context-management tooling, and governance mechanisms that can be used broadly across teams
• The right person for this role thinks in terms of platform as a product. You care not only about technical correctness, but also adoption, ergonomics, standardization, and long-term leverage. You know how to create opinionated golden paths that help teams move faster without sacrificing reliability, observability, or governance
• Build the AI platform foundations: Design and maintain the core APIs, SDKs, libraries, and services that standardize how teams at Invoca build AI and agentic capabilities
• Create reusable agentic platform primitives: Build the platform components that product teams can assemble into real applications, such as orchestration building blocks, tool interfaces, workflow contracts, context-handling systems, and retrieval primitives
• Own platform-level evaluation infrastructure: Build the shared evaluation framework that enables teams to test AI systems consistently and at scale. This includes CI/CD integration, dataset and experiment workflows, scoring pipelines, and support for offline and online evaluation
• Standardize orchestration and interoperability: Help define the contracts, schemas, and interfaces that allow tools, runtimes, and services to work together cleanly. This includes platform patterns for MCP-style interoperability, tool discovery, and agent-to-agent or agent-to-system interactions
• Build for production scale and reliability: Design and improve the serving, routing, and control layers that sit between applications and model providers. You will help ensure low latency, high availability, cost efficiency, and strong production behavior.Drive observability and governance
• Build and enforce the platform capabilities that make AI systems measurable, debuggable, and governable in production, including tracing, auditing, policy enforcement, and operational standards.Improve developer experience and platform adoption: Treat engineers and product teams as customers. Create tools, templates, documentation, and reference implementations that make the platform easy to adopt and hard to misuse
• Shape technical direction: As a staff engineer, you will influence architecture, standards, and long-term technical strategy for AI Platform. You will identify repeated patterns across teams and convert them into reusable platform solutions
• As a Staff AI Platform Engineer, you will help define the technical foundation for AI development at Invoca
• You will not be building isolated AI features; you will be creating the shared platform capabilities, standards, and operating model that enable many teams to build AI products quickly, safely, and consistently. You will:
• Architect the AI platform foundations: Design and evolve the core libraries, SDKs, APIs, and shared services that standardize how teams build agentic and GenAI-powered products at Invoca
• Build reusable agentic and retrieval primitives: Create modular platform components for orchestration, retrieval, tool execution, workflow composition, adoption of latest protocols and standards, and A2A/MCP-compatible interoperability that product teams can use to build production-grade AI systems
• Own the evaluation platform: Build the shared evaluation infrastructure that enables teams to measure and improve AI quality through datasets, scoring pipelines, experiment workflows, and CI/CD-integrated offline and online evaluations
• Standardize context engineering: Develop the systems and tooling for prompt management, context versioning, templating, testing, and governance so teams can iterate safely and consistently on AI applications
• Engineer for scale, reliability, and governance: Build/Establish the serving, routing, and control layers between applications and model providers, ensuring strong production performance, cost efficiency, policy enforcement, and operational resilience
• Drive technical strategy and platform adoption: Partner with product and engineering leaders to turn recurring architectural needs into shared standards, reference implementations, and developer-friendly golden paths that accelerate delivery across the organization