Appier is seeking an Agentic AI Engineering Manager to lead a team building agentic and recommendation products for next-generation digital marketing. The role involves leading engineering efforts at the intersection of ML systems, LLM/agent orchestration, and enterprise SaaS to deliver measurable outcomes for marketers, including faster execution, smarter budget allocation, and improved brand performance in Tokyo, Japan with relocation required.
What You'll Do
- Lead a team of engineers and partner closely with ML scientists to deliver enterprise-grade agent and recommendation capabilities.
- Translate product goals into technical strategy, milestones, and execution plans; drive delivery with high quality and predictable cadence.
- Establish engineering excellence: code quality, testing, observability, incident response, and continuous improvement.
- Build an environment of humble, hungry, smart execution—measure outcomes, iterate quickly, reduce waste.
- Design and ship agent frameworks: planning/execution loops, tool calling, memory, retrieval, safety guardrails, and evaluation.
- Define evaluation methodology for agents: offline benchmarks, online experiments, success metrics, and human-in-the-loop review flows.
- Assure solid integration with One Data Platform: data contracts, feature availability, privacy controls, and reliability SLAs.
- Partner with Product, Design, Data/ML, and GTM to ensure model behavior aligns with user needs and business goals.
- Drive stakeholder alignment: connect engineering objectives to business outcomes (revenue, retention, customer value).
- Promote an autonomous culture: empower autonomous decision-making for both the team and the systems you build.
- Manage fast-paced experimentation while ensuring security, compliance, and enterprise readiness.
What We're Looking For
- 8+ years professional experience as a Software Engineer
- 4+ years in a management/leadership role
- Strong communication in English (written and verbal)
- Degree in Computer Science / Informatics or equivalent practical experience
- Proven track record delivering enterprise-grade products (reliability, security, observability, performance)
- Experience working with or managing engineers and ML scientists
- Experience delivering solutions using Agile principles
Nice to Have
- Experience building LLM/agentic systems in production (tool use, RAG, orchestration, evaluation)
- Hands-on background with ML systems (recommendation, ranking, attribution, uplift, LTV, forecasting) and their productionization
- Familiarity with data platforms (event pipelines, identity resolution, feature stores, lakehouse/warehouse) and data governance
- Experience with A/B testing and causal measurement; comfortable making tradeoffs using metrics
- Exposure to MA/CRM domains: segmentation, journeys, omnichannel messaging, campaign ops, budget pacing
- Experience in leading hybrid (onsite/remote) engineering team
Technical Stack
- LLM/agent orchestration
- ML systems
- One Data Platform
- enterprise SaaS
- RAG
- tool calling
- retrieval
- memory
- evaluation frameworks
- data contracts
- feature stores
- event pipelines
- identity resolution
- lakehouse/warehouse
- A/B testing
- causal measurement
Team & Environment
- Engineering team collaborating with ML scientists, Product, Design, Data/ML, and GTM teams
Humble, hungry, smart execution; measure outcomes, iterate quickly, reduce waste; improve through constant measurement and feedback; innovate by failing well; adapt quickly; stay efficient and responsive.
Benefits & Compensation
- Relocation required and supported (inferred from job title)
Work Mode
- onsite
- locations: Tokyo, Japan
- Relocation required; no remote flexibility indicated
