Senior AI Solutions Engineer

Crunchyroll, LLC • Los Angeles, CA

1d • $183,400 - $229,200 • Hybrid

About The Position

We're looking for a Senior AI Solutions Engineer to embed with teams across Crunchyroll and build AI solutions that materially change how work gets done. You'll go where the work is. You'll sit with teams, understand how their workflows actually operate, identify where AI or automation can create meaningful value, and rapidly build solutions alongside the people who will use them. This role combines strong business judgment and consultative problem-solving with hands-on engineering. You'll take problems from ambiguity through prototype, production, and adoption. You'll be part of AI Enablement in the Executive Office, working across Crunchyroll to turn high-value business problems into practical AI-powered solutions, including agents, workflow automation, and other applied AI systems. You'll connect models to the systems, tools, and data teams already use, and build the context, evaluations, guardrails, and human checkpoints required for them to work reliably in the real world. The goal is not to build AI for its own sake. You'll focus on problems where AI can materially improve speed, quality, cost, or the employee experience, and you'll measure whether what you build actually delivers that impact. You'll work closely with Engineering, Enterprise Technology, Data & Insights, IT, Security, and other partners to ensure solutions are scalable, secure, and maintainable. Every engagement should also leave behind reusable components, patterns, and lessons that make the next solution faster to build.

Requirements

• 6+ years of software engineering experience, including significant recent experience building and shipping applied AI or LLM-powered systems in production.
• Demonstrated experience designing, building, and deploying AI systems that interact with APIs, enterprise systems, data sources, or external tools to complete multi-step tasks.
• Strong Python skills and deep experience with modern LLM application development, including tool and function calling, orchestration, prompt and context engineering, structured outputs, RAG, model APIs, state management, and enterprise integrations.
• Experience taking AI solutions end-to-end, from an ambiguous problem statement through architecture, prototyping, implementation, deployment, and iteration.
• Experience engineering AI systems for reliability, including failure handling, human-in-the-loop controls, guardrails, permissions, monitoring, cost management, and latency management.
• Strong technical judgment on when to use an agent, RAG, classical ML, automation, or conventional software, including the discipline to avoid unnecessary complexity.
• Proven ability to work directly with non-technical stakeholders, understand how their work actually happens, and translate business needs into practical technical solutions.
• Strong product and business judgment. You can distinguish an interesting technical problem from a problem worth solving.
• Comfort operating in ambiguous, early-stage environments where you are expected to define the problem as much as solve it.
• The ability to move between rapid experimentation and production engineering, knowing when speed matters and when reliability, security, and scale matter more.

Nice To Haves

• Experience in a forward-deployed, embedded, solutions engineering, applied AI, or highly cross-functional engineering role.
• Experience building tool-using or agentic systems that plan and execute multi-step workflows.
• Experience with APIs, MCP, event-driven systems, enterprise SaaS integrations, and internal data systems.
• Experience building internal platforms, SDKs, reusable tooling, or shared components adopted by other engineering teams.
• Experience evaluating and observing LLM or agent systems, including eval design, tracing, monitoring, and drift detection using tools such as Datadog, LangSmith, Langfuse, or similar platforms.
• Experience designing multi-agent systems, planner/executor architectures, specialized agents, or other orchestration patterns where they are appropriate.
• Familiarity with workflow analysis and process mapping, particularly in complex operational environments.
• Exposure to AI governance, security, privacy, and legal review processes, including risks related to data access, permissions, prompt injection, tool use, autonomous actions, and sensitive information.
• Experience in media, entertainment, streaming, consumer technology, or another environment with complex content and operational workflows. Familiarity with areas such as localization, marketing operations, content operations, customer support, rights management, or commerce is a plus.

Responsibilities

• Embed deeply with business teams. Sit alongside them as work happens, map the real workflow rather than the documented one, and understand the pain points, decisions, systems, and handoffs that shape how work gets done.
• Identify where AI can create meaningful business value. Start with the problem, not the technology, and be willing to conclude that AI is not the right answer.
• Determine the right technical approach for each problem, whether that is an agentic system, generative AI, RAG, classical ML, deterministic automation, or a simpler software solution.
• Translate ambiguous business problems into clear technical plans, including architecture, data flows, integration points, permissions, tool usage, success criteria, and build vs. buy vs. integrate decisions.
• Prototype quickly to test whether an idea works before over-engineering it. Know when to build something in days to learn and when a problem warrants production-grade infrastructure.
• Design and build reliable AI systems end-to-end, including tool use, orchestration, context and prompt engineering, state and memory, structured outputs, APIs, enterprise integrations, and human-in-the-loop workflows.
• Engineer for the real world, not the demo. Anticipate failure modes, tool errors, hallucinations, retries, permission boundaries, latency, cost, security constraints, and graceful degradation.
• Build evaluations and observability from the start. Define task-level success criteria and eval sets, and instrument tracing, logging, monitoring, cost, quality, and drift so performance is measurable in production.
• Define the expected business outcome before you build and measure whether the solution actually improves the workflow after launch.
• Work directly with business and technical leaders, translating between business needs and technical reality, setting clear expectations, surfacing risks early, and keeping stakeholders aligned as the solution evolves.
• Stay close to users after launch. Observe how solutions perform in real workflows, iterate based on usage and feedback, and partner with the broader AI Enablement team on adoption and change management.
• Build for handoff and scale. Establish clear ownership, documentation, monitoring, and maintenance paths so successful solutions can transition to the appropriate long-term owner.
• Create reusable components, patterns, tools, and learnings that make future AI solutions faster and easier to build across Crunchyroll.

Benefits

• Great compensation package including salary plus performance bonus earning potential, paid annually.
• Flexible time off policies allowing you to take the time you need to be your whole self.
• Generous medical, dental, vision, STD, LTD, and life insurance
• Health Saving Account HSA program
• Health care and dependent care FSA
• 401(k) plan, with employer match
• Employer paid commuter benefit
• Support program for new parents
• Pet insurance and some of our offices are pet friendly!

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed