Overview:
The Forward Deployed Engineer (FDE) is responsible for designing, building, and deploying AI-powered applications in close collaboration with customers, bridging the gap between business problems and production-ready technical solutions. This role combines hands-on software engineering, applied AI implementation, and direct customer engagement, ensuring that solutions are technically robust, operationally scalable, and aligned with business outcomes.
The FDE works at the intersection of application engineering, AI systems, data workflows, and customer delivery, partnering directly with client stakeholders, product teams, and internal engineering teams to rapidly translate requirements into working solutions. The role requires strong technical depth in modern application development, cloud-native systems, and Generative AI implementation, along with the ability to operate effectively in ambiguous and fast-moving delivery environments.
Responsibilities:
- Engage directly with customers to understand business challenges, technical requirements, and operational constraints
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Translate customer requirements into solution designs, technical workflows, and implementation plans
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Design, develop, and deploy production-grade applications using Python, JavaScript, or related technologies
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Build and integrate LLM-powered applications, including conversational systems, automation workflows, and knowledge-based systems
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Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding strategies, retrieval optimization, and response orchestration
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Build and manage agent-based architectures, including task orchestration, tool integration, and execution flows
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Design and maintain evaluation frameworks to measure model quality, retrieval effectiveness, and output reliability
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Implement and manage MLOps/LLMOps pipelines covering deployment, monitoring, versioning, rollback, and lifecycle management
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Develop and deploy applications in cloud environments such as AWS, Azure, or GCP
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Collaborate with data engineers, architects, and technical leads to integrate AI workflows into enterprise systems
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Identify technical risks and implementation bottlenecks, proposing mitigation strategies proactively
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Balance rapid prototyping with production-readiness, ensuring quality, scalability, and maintainability
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Support optimization of delivery workflows by leveraging AI tools to improve engineering productivity and operational efficiency
Requirements:
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Minimum 8 years of experience in software engineering, technical implementation, or related technical delivery roles
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Proven experience driving projects with direct client engagement and stakeholder management
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Experience working in fast-paced, ambiguous delivery environments with strong ownership and execution capability
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Strong hands-on development experience using Python and/or JavaScript
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Experience building production-grade backend services, APIs, and application workflows
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Strong understanding of software engineering fundamentals including modular design, testing, and maintainability
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Experience integrating frontend and backend components for end-to-end solution delivery
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Demonstrated experience building or implementing applications leveraging Large Language Models (LLMs) and Generative AI technologies
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Practical experience designing and implementing Retrieval-Augmented Generation (RAG) workflows
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Experience in agent design, orchestration logic, and tool-based execution patterns
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Experience building evaluation frameworks for model validation, retrieval quality, and output consistency
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Understanding of prompt engineering, model behavior optimization, and AI system reliability
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Experience building and deploying systems in cloud environments (AWS, GCP, Azure)
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Experience with containerized deployment and cloud-native architecture patterns
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Understanding of deployment automation, CI/CD pipelines, and infrastructure provisioning
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Familiarity with application scalability, resilience, and cloud cost optimization
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Experience designing and operating MLOps and/or LLMOps pipelines
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Understanding of model lifecycle management including deployment, versioning, monitoring, and rollback
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Experience implementing observability for AI systems, including performance and quality monitoring
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Strong ability to engage with customers to clarify requirements, align expectations, and drive delivery decisions
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Ability to explain complex technical concepts to business and non-technical stakeholders
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Experience balancing customer priorities with technical feasibility and delivery constraints
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Strong problem-solving orientation with a customer-first mindset
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Ability to balance hands-on coding with customer-facing engagement
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Strong decision-making capability under ambiguity
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Ability to maintain delivery speed without compromising quality
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Strong ownership and accountability for delivery outcomes
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Ability to remain composed and effective in high-pressure delivery environments
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Business-level or higher Japanese language proficiency
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Experience implementing large-scale enterprise systems
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Experience in data security, governance, and access control design
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Experience working in startup or new business environments
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Experience collaborating with global or distributed teams
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Strong English communication skills
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Experience with observability tools and operational monitoring for AI systems
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English: Business proficiency required
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Japanese: Business-level proficiency preferred
