Lead AI Engineer

Appealing Points:

• Lead AI and cloud transformation initiatives by designing Azure-based, agentic AI, and LLM-powered solutions from concept to production.
• Drive end-to-end solution delivery by leading business workshops, defining MVPs, and collaborating with business, engineering, and architecture teams.
• Work with cutting-edge technologies including Azure, Python, LangChain, Kubernetes, CI/CD, and cloud-native architectures while mentoring development teams.

Annual Salary: 7 Million Yen and above

Job Responsibilities:

• Lead business requirement definition workshops; ask the right questions to uncover user pain points, operational constraints, success metrics, and decision criteria.
• Translate high-level ideas into clear user stories, acceptance criteria, solution scope, MVP definition, delivery roadmap, and backlog priorities.
• Create audience-appropriate visual materials such as solution diagrams, process flows, architecture views, MVP comparisons, and decision papers.
• Facilitate multi-round discussions with business, IT, risk/compliance, security, architecture, and vendor teams to align on feasible solutions.
• Support build-vs-buy analysis, including technical feasibility, integration complexity, maintainability, delivery risk, operating model, and cost considerations.
• Lead hands-on solution design and delivery for Azure/cloud-based AI and agentic applications.
• Provide technical leadership across Python, data pipelines, LLM orchestration, CI/CD, containerization, and cloud-native engineering practices.
• Guide engineers through design reviews, code reviews, testing strategy, deployment readiness, production support planning, and continuous improvement.
• Ensure agile delivery discipline: sprint planning, backlog refinement, dependency tracking, stakeholder demos, and transparent status communication.

Job Qualifications:

• Cloud-based solutioning and development experience; Azure experience strongly preferred, with AWS or Google Cloud also valuable.
• Python application development for backend services, automation, AI/ML workflows, or data processing.
• Data engineering experience, including data pipelines, ETL/ELT patterns, API integration, data quality checks, and secure data handling.
• Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or similar agentic/LLM application frameworks.
• Practical understanding of LLM usage, including prompt engineering, context engineering, evaluation, guardrails, retrieval-augmented generation, and model behavior analysis.
• CI/CD experience using GitHub Enterprise, GitHub Actions, Azure DevOps, or equivalent tooling.
• Containerization and orchestration experience using Docker and Kubernetes.
• API design, microservices, authentication/authorization, observability, logging, and operational monitoring fundamentals.
• Understanding of enterprise security, privacy, compliance, and production change-management expectations.
• MVP definition and delivery planning: ability to identify the minimum viable product, define scope boundaries, prioritize features, validate assumptions, and create a practical roadmap from prototype to production delivery.
• Model Context Protocol (MCP) understanding and hands-on ability to design secure tool/resource integration patterns for agentic applications.
• Strong consultative communication: able to guide business users who do not yet have detailed requirements.
• Business empathy and problem-framing: able to understand pain points in business terms before jumping to technology.
• Facilitation and negotiation skills across business, technology, risk, compliance, architecture, and vendor stakeholders.
• Ability to simplify complex AI/cloud topics for non-technical audiences and provide enough depth for engineering teams.
• Proactive ownership mindset; comfortable driving ambiguous topics to concrete decisions and deliverables.
• Coaching mindset: able to mentor junior engineers and improve team delivery capability.
• Strong written communication for decision papers, diagrams, requirements, status updates, and executive summaries.

Preferred Qualifications:

• Experience in insurance, financial services, customer service, call center, underwriting, claims, producer support, or policy administration projects.
• Experience working with remote and overseas members across different time zones, cultures, and delivery models.
• Japanese business communication ability is a strong plus for Japan-based stakeholder engagement.

Langauge Required: Fluent level Japanese (JLPT N1) and Business level English proficiency

Company Description:

We are a technology-led software services company that fulfills technology companies' software product development needs worldwide. As a trusted partner of today’s enterprises, we build software solutions for the future. We aim to understand our customers' needs and aspirations to deliver software that aligns with their vision. We are passionate about creating future-ready software focusing on superior user experience and ultimate simplicity.Specializing in mobile, web, and cloud-based software development services across various industry verticals, we cater to customers globally. We focus on developing next-generation software products that require a deep understanding of communication networks, operating system internals, device diversity, and critical system resources. Our core strength lies in combining these engineering environments to create cutting-edge software solutions.

. Skillset Required: Azure, Python, LangChain, Kubernetes, CI/CD, Cloud-native architectures, GitHub Enterprise, GitHub Actions, Azure DevOps, Docker, LangGraph, Semantic Kernel, AutoGen, Prompt engineering, Context engineering, Evaluation, Guardrails, Retrieval-augmented generation, Model behavior analysis, Data engineering, Data pipelines, ETL/ELT, API integration, Data quality checks, Secure data handling, API design, Microservices, Authentication/authorization, Observability, Logging, Operational monitoring, Enterprise security, Privacy, Compliance, Production change-management, MVP definition and delivery planning, Model Context Protocol (MCP), Consultative communication, Business empathy, Problem-framing, Facilitation and negotiation, Coaching, Written communication, Insurance, Financial services, Customer service, Call center, Underwriting, Claims, Producer support, Policy administration, Japanese business communication, Backend services, Automation, AI/ML workflows, Data processing, Containerization, Agile methodologies, Sprint planning, Backlog refinement, Stakeholder demos, Build-vs-buy analysis, Technical writing