Forward Deployed Engineer, AI Enablement

Description

The company is the company, which brings anime and manga to over 100 million fans in more than 200 countries and territories, across streaming, theatrical, games, merchandise and events.

This role sits in AI Enablement within the Executive Office. You will build AI solutions across the company that change how work gets done, including agents, workflow automation and other applied AI systems.

You will embed with teams, learn how their work really runs, and build solutions with them, taking each problem from an unclear starting point through prototype, production and adoption.

The aim is not AI for its own sake. You will pick problems where AI can improve speed, quality, cost or the employee experience, and measure whether the result delivers that.

You will work with Engineering, Enterprise Technology, Data & Insights, IT, Security and other partners so solutions are scalable, secure and maintainable. Each project should also leave reusable parts and lessons for the next one.

The Executive Office drives enterprise alignment and executive execution, keeping the company's highest-priority work moving quickly and clearly.

Responsibilities

• Spend time with business teams while they work, map the workflow as it actually runs rather than as documented, and pin down the pain points, decisions, systems and handoffs involved.
• Decide whether AI fits the problem at all, and be ready to conclude it does not.
• Choose the right approach for each problem, such as an agent, generative AI, RAG, classical ML, deterministic automation or plain software.
• Turn vague business problems into concrete technical plans covering architecture, data flow, integrations, permissions, tool use, success criteria and whether to build, buy or integrate.
• Prototype fast to test an idea before investing in it, and recognise when a problem needs production-grade infrastructure.
• Build AI systems end to end: tool use, orchestration, context and prompt design, state and memory, structured outputs, APIs, enterprise integrations and human-in-the-loop steps.
• Plan for failure, including tool errors, hallucinations, retries, permission limits, latency, cost, security and graceful fallback.
• Set task-level success criteria and eval sets from the start, and instrument tracing, logging, monitoring, cost, quality and drift.
• Agree the expected business result before building, then check after launch whether the workflow actually improved.
• Translate between business and technical leaders, set expectations, raise risks early and keep stakeholders aligned as the solution changes.
• Stay close to users after launch, watch how the solution performs in real use, iterate on usage and feedback, and work with the AI Enablement team on adoption and change management.
• Prepare each solution for handoff with clear ownership, documentation, monitoring and a maintenance path to its long-term owner.
• Build reusable components, patterns, tools and lessons that speed up future AI work across the company.

Requirements

• At least 6 years of software engineering experience, with significant recent work building and shipping applied AI or LLM-based systems in production.
• Proven record designing, building and deploying AI systems that call APIs, enterprise systems, data sources or external tools to complete multi-step tasks.
• Strong Python and solid experience with LLM application development, covering tool and function calling, orchestration, prompt and context design, structured outputs, RAG, model APIs, state management and enterprise integrations.
• Experience carrying an AI solution from a vague problem through architecture, prototype, build, deployment and iteration.
• Experience making AI systems reliable, including failure handling, human-in-the-loop controls, guardrails, permissions, monitoring, cost control and latency control.
• Sound judgment on when to use an agent, RAG, classical ML, automation or conventional software, and the discipline to avoid needless complexity.
• Proven ability to work directly with non-technical stakeholders, understand how their work happens and turn their needs into practical technical solutions.
• Strong product and business judgment, able to tell an interesting technical problem from one worth solving.
• Comfort in ambiguous, early-stage settings where you help define the problem as well as solve it.
• Ability to move between quick experimentation and production engineering, knowing when speed matters and when reliability, security and scale matter more.
• Nice to have: experience in a forward-deployed, embedded, solutions engineering, applied AI or highly cross-functional engineering role.
• Nice to have: experience building tool-using or agentic systems that plan and carry out multi-step workflows.
• Nice to have: experience with APIs, MCP, event-driven systems, enterprise SaaS integrations and internal data systems.
• Nice to have: experience building internal platforms, SDKs, reusable tooling or shared components that other engineering teams adopted.
• Nice to have: experience evaluating and observing LLM or agent systems, including eval design, tracing, monitoring and drift detection with tools such as Datadog, LangSmith, Langfuse or similar.
• Nice to have: experience designing multi-agent systems, planner/executor architectures, specialised agents or other orchestration patterns where they fit.
• Nice to have: familiarity with workflow analysis and process mapping, especially in complex operational settings.
• Nice to have: exposure to AI governance, security, privacy and legal review, including risks around data access, permissions, prompt injection, tool use, autonomous actions and sensitive information.
• Nice to have: experience in media, entertainment, streaming, consumer technology or another setting with complex content and operational workflows. Familiarity with localization, marketing operations, content operations, customer support, rights management or commerce is a bonus.

Conditions

Pay range is $183,400 - $229,200 USD. Actual pay depends on location, experience and performance.

Hybrid work model, listed as LI-Hybrid.

You will not work from a central backlog waiting for requirements. You will go to the highest-value problems, embed with the affected teams and stay until the solution delivers measurable value.

Some engagements end as a lightweight automation built in days, while others become production AI systems used company-wide. You decide which is which and build accordingly.

The company is an equal opportunity employer and does not discriminate on protected characteristics as required by applicable law.

Benefits

• Compensation includes base salary plus performance bonus potential, paid annually.
• Flexible time off so you can take the time you need.
• Medical, dental, vision, short- and long-term disability, and life insurance.
• HSA and health care and dependent care FSA options.
• 401(k) with employer match.
• Employer-paid commuter benefit.
• Support program for new parents.
• Pet insurance, and some offices are pet friendly.