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
- 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
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**
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- 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**
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- 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
- *Language Requirements**
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- **English:** Business proficiency required
- **Japanese:** Business\-level proficiency preferred