• 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

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 preferred