Senior AI Solutions Engineer (AI Enablement)

Join Crunchyroll as a Senior AI Solutions Engineer, where you'll work closely with various teams to identify and implement AI solutions that enhance productivity and efficiency. This role requires a combination of strong business acumen, consultative problem-solving, and hands-on engineering skills. You'll be responsible for translating ambiguous business problems into clear technical plans, prototyping solutions, and ensuring their successful implementation and adoption.

USD 183,400 - 229,200

Responsibilities

• Identifying and implementing AI solutions that enhance workflows and create meaningful business value.
• Collaborating with cross-functional teams to understand their needs and translate them into practical technical solutions.
• Prototyping and building reliable AI systems end-to-end, from architecture to deployment, while ensuring scalability and maintainability.
• Proven ability to work directly with non-technical stakeholders, understand how their work actually happens, and translate business needs into practical technical solutions

Requirements

• 6+ years of software engineering experience, including significant recent experience building and shipping applied AI or LLM-powered systems in production
• 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
• Demonstrated experience designing, building, and deploying AI systems that interact with APIs, enterprise systems, data sources, or external tools to complete multi-step tasks
• Experience taking AI solutions end-to-end, from an ambiguous problem statement through architecture, prototyping, implementation, deployment, and iteration
• The ability to move between rapid experimentation and production engineering, knowing when speed matters and when reliability, security, and scale matter more
• 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 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
• 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