Forward Deployed Engineer ( AI)

Job Description: Forward Deployed Engineer (FDE)

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.

Roles & 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

Language
Requirements

- English: Business proficiency required
- Japanese: Business-level proficiency N1 & N2 preferred