Senior AI Solutions Engineer

About this role

About Crunchyroll

Founded by fans, Crunchyroll delivers the art and culture of anime to a passionate community. We super-serve over 100 million anime and manga fans across 200+ countries and territories, and help them connect with the stories and characters they crave. Whether that experience is online or in-person, streaming video, theatrical, games, merchandise, events and more, it’s powered by the anime content we all love.

Join our team, and help us shape the future of anime!

As a Senior AI Solutions Engineer in Crunchyroll's AI Enablement "Build & Partner" layer, you are the dedicated AI partner for business functions like Content, Marketing, or Product. You embed within these teams to deliver tailored solutions based on their specific operational context.

This hands-on role combines engineering with consulting, focusing on building production-grade agentic systems. You design and implement systems that plan, use tools, and maintain state - utilizing RAG pipelines and workflow automation, rather than just building simple chat interfaces. Your time in this role is split between stakeholder scoping and technical implementation.

You own the full lifecycle, from task decomposition and model selection to orchestration, evaluations, and production deployment. You build on the central AI Enablement team's shared technical standards, approved tooling list, and architectural playbook, and feed your own patterns, reusable components, and lessons learned back into them so the whole organization benefits from what you ship.

In this role, you will

• Embed deeply with your assigned business function - attend their meetings, map their workflows end-to-end, and learn their pain points firsthand before proposing changes

• Ask the most important question first: "Should we use AI here at all?" - be willing to say no, and when the answer is yes, choose the right kind of AI (agentic system, generative AI, classical ML, RAG, or simple automation) rather than defaulting to the most sophisticated option

• Spot which parts of a workflow are genuinely agent-shaped - multi-step, tool-using, decisioning tasks where autonomy adds value, versus where a deterministic automation or a single LLM call is the more honest answer

• Translate business problems into clear technical designs - architecture diagrams, data flow maps, integration points, tool selection, and explicit build vs. buy vs. integrate decisions you can defend to both technical and non-technical audiences

• Design agentic systems deliberately: decompose the task, define the agent's goals, tools, and action space, choose between single-agent and multi-agent orchestration, and specify how it plans, reasons, retains memory/state, escalates to a human, and stays within its guardrails

• Engineer agents for reliability rather than demos: handle failure modes, retries, tool errors, hallucination recovery, cost and latency budgets, human-in-the-loop checkpoints, and graceful degradation when a step fails

• Build evaluation and observability from day one - define task-level success criteria and eval sets, and instrument tracing, logging, and monitoring so agent behavior, quality, cost, and drift are measurable in production rather than assumed

• Manage stakeholders proactively: act as the translator between business needs and technical reality, set honest expectations about what agents can and cannot reliably do, surface risks early, prevent scope creep, and never let a stakeholder be surprised

• Support post-handoff sustainability - what you build should still be operational and maintained well after you've moved to the next engagement

What We're Looking For

Required

• 6+ years of software engineering experience, with at least 3 years building applied AI/agentic solutions in a production environment.

• Demonstrated experience designing, building, and shipping reliable agentic systems to production: tool-using agents that plan, call tools/APIs and MCP servers, and complete multi-step tasks.

• You've owned the reliability engineering that keeps them running, including failure handling, human-in-the-loop control, guardrails, and cost and latency management.

• Experience building internal platforms, SDKs, or tooling adopted by other teams, with the design, documentation, and usability that drive real adoption.

• Strong Python skills and deep command of the agentic stack: one or more agentic frameworks (LangChain/LangGraph, LlamaIndex, AutoGen, CrewAI, the OpenAI Agents SDK, or similar), tool/function calling, orchestration, and state/memory management. It also covers prompt and context engineering, RAG architectures, vector databases, LLM APIs, structured outputs, and integrations with internal and third-party systems.

• Able to build AI solutions end-to-end - you can go from problem statement through architecture and design to a working solution without relying on another engineer to execute your designs.

• Sound judgment on whether to use AI at all, and which kind to use (agent, RAG, classical ML, or simple automation), including the discipline to say no rather than default to the most sophisticated option.

• Proven ability to work directly with non-technical stakeholders and manage expectations.

• Comfort operating in ambiguous, early-stage environments where you define the problem as much as you solve it, starting from a vague complaint, working back to a crisp technical plan, and knowing when to prototype quickly versus when to build for the long term.

Strongly Preferred

• Experience designing multi-agent systems, coordinating multiple specialized agents, planner/executor patterns, and agent-to-agent or protocol-based tool interoperability.

• Experience in an embedded, consultative, or cross-functional role - you understand what it means to serve another team's goals rather than your own backlog.

• Familiarity with workflow analysis and process mapping - you've documented how work actually gets done before proposing how to chang

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