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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
Translate customer requirements into solution designs, technical workflows, and implementation plans
Design, develop, and deploy production-grade applications using Python, JavaScript, or related technologies
Build and integrate LLM-powered applications, including conversational systems, automation workflows, and knowledge-based systems
Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding strategies, retrieval optimization, and response orchestration
Build and manage agent-based architectures, including task orchestration, tool integration, and execution flows
Design and maintain evaluation frameworks to measure model quality, retrieval effectiveness, and output reliability
Implement and manage MLOps/LLMOps pipelines covering deployment, monitoring, versioning, rollback, and lifecycle management
Develop and deploy applications in cloud environments such as AWS, Azure, or GCP
Collaborate with data engineers, architects, and technical leads to integrate AI workflows into enterprise systems
Identify technical risks and implementation bottlenecks, proposing mitigation strategies proactively
Balance rapid prototyping with production-readiness, ensuring quality, scalability, and maintainability
Support optimization of delivery workflows by leveraging AI tools to improve engineering productivity and operational efficiency
Requirements:
Experience
Minimum 8 years of experience in software engineering, technical implementation, or related technical delivery roles
Proven experience driving projects with direct client engagement and stakeholder management
Experience working in fast-paced, ambiguous delivery environments with strong ownership and execution capability
Application Engineering
Strong hands-on development experience using Python and/or JavaScript
Experience building production-grade backend services, APIs, and application workflows
Strong understanding of software engineering fundamentals including modular design, testing, and maintainability
Experience integrating frontend and backend components for end-to-end solution delivery
Applied AI Engineering
Demonstrated experience building or implementing applications leveraging Large Language Models (LLMs) and Generative AI technologies
Practical experience designing and implementing Retrieval-Augmented Generation (RAG) workflows
Experience in agent design, orchestration logic, and tool-based execution patterns
Experience building evaluation frameworks for model validation, retrieval quality, and output consistency
Understanding of prompt engineering, model behavior optimization, and AI system reliability
Cloud & Platform Engineering
Experience building and deploying systems in cloud environments (AWS, GCP, Azure)
Experience with containerized deployment and cloud-native architecture patterns
Understanding of deployment automation, CI/CD pipelines, and infrastructure provisioning
Familiarity with application scalability, resilience, and cloud cost optimization
MLOps / LLMOps
Experience designing and operating MLOps and/or LLMOps pipelines
Understanding of model lifecycle management including deployment, versioning, monitoring, and rollback
Experience implementing observability for AI systems, including performance and quality monitoring
Customer Delivery & Stakeholder Engagement
Strong ability to engage with customers to clarify requirements, align expectations, and drive delivery decisions
Ability to explain complex technical concepts to business and non-technical stakeholders
Experience balancing customer priorities with technical feasibility and delivery constraints
Soft Skills
Strong problem-solving orientation with a customer-first mindset
Ability to balance hands-on coding with customer-facing engagement
Strong decision-making capability under ambiguity
Ability to maintain delivery speed without compromising quality
Strong ownership and accountability for delivery outcomes
Ability to remain composed and effective in high-pressure delivery environments
Nice to Have
Business-level or higher Japanese language proficiency
Experience implementing large-scale enterprise systems
Experience in data security, governance, and access control design
Experience working in startup or new business environments
Experience collaborating with global or distributed teams
Strong English communication skills
Experience with observability tools and operational monitoring for AI systems