Overview:
The Technical Lead is responsible for owning the technical execution and engineering delivery of complex application and AI-enabled solutions across the software development lifecycle. This role serves as the primary technical authority for the engineering team, translating architecture and business requirements into executable technical plans, ensuring delivery quality, and guiding engineers through implementation.
The Technical Lead manages the day-to-day technical delivery of engineering teams comprising junior, mid-level, and senior software engineers, working closely with Forward Deployed Engineers (FDEs) on customer-driven solution implementation and Technical Program Managers (TPMs) on delivery planning, milestone tracking, and dependency management.
The role requires strong expertise in system design, cloud-native development, AI application integration, engineering quality practices, and technical mentoring, with accountability for technical stability, delivery predictability, and implementation excellence.
Responsibilities:
- Own the technical execution of software delivery across engineering workstreams, ensuring alignment with business requirements and solution architecture
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Break down high-level architecture into implementable technical components, APIs, services, and engineering tasks
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Lead system design discussions across application, data, and integration layers
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Define and enforce engineering standards across development, testing, deployment, and documentation
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Guide and support SDE1, SDE2, and SDE3 engineers in implementation, debugging, and technical problem-solving
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Conduct code reviews and ensure adherence to coding standards, maintainability, and architectural integrity
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Collaborate with FDEs on AI application integration patterns including LLM workflows, RAG pipelines, and agent orchestration
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Work with TPMs to support effort estimation, technical dependency planning, and delivery sequencing
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Identify technical risks early and define mitigation strategies across system design, integrations, and delivery execution
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Ensure implementation of testing strategies including unit testing, integration testing, contract testing, and performance validation
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Oversee CI/CD implementation and deployment readiness across development environments
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Ensure observability, monitoring, and operational readiness are embedded into engineering solutions
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Support incident troubleshooting and root cause analysis for production issues
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Mentor engineers and support technical capability growth across the team
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Improve team productivity by promoting engineering accelerators, automation, and effective use of AI-assisted development tools
Requirements:
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6 to 10 years of software engineering experience
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At least 2 to 4 years of technical leadership experience leading software engineering teams or major system components
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Experience delivering enterprise-grade systems in complex engineering environments
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Strong hands-on expertise in system design and application architecture
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Strong understanding of microservices architecture, service boundaries, and API contracts
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Experience in distributed systems design, asynchronous processing, and event-driven patterns
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Strong understanding of design patterns, SOLID principles, and modular application design
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Experience designing systems for scalability, fault tolerance, and maintainability
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Strong hands-on backend development experience using Python and/or JavaScript/TypeScript
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Strong expertise in FastAPI or equivalent backend frameworks
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Strong understanding of REST API principles, service orchestration, and integration patterns
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Experience with asynchronous workflows and backend performance optimization
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Working knowledge of frontend development using React and modern UI frameworks
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Understanding of frontend integration patterns and real-time interaction mechanisms (e.g., SSE)
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Ability to guide frontend engineers on implementation quality and architecture alignment
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Working knowledge of LLM-based application development and applied AI integration patterns
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Understanding of RAG architecture, retrieval optimization, and evaluation loops
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Familiarity with guardrails, prompt workflows, and AI reliability mechanisms
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Ability to support engineering teams implementing AI-enabled application workflows
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Strong understanding of data modeling and data flow design
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Experience with SQL and NoSQL databases
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Understanding of data pipelines, transformations, and data quality practices
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Experience handling structured and semi-structured application data
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Strong experience with Docker, Kubernetes, and containerized deployments
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Strong understanding of CI/CD pipelines and release automation
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Experience with Infrastructure-as-Code (Terraform)
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Experience deploying applications in cloud environments (AWS, Azure, GCP)
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Strong understanding of environment management and release strategies
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Strong understanding of Test Driven Development (TDD)
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Experience implementing unit, integration, and end-to-end testing strategies
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Strong debugging and troubleshooting capability
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Experience driving engineering quality improvements across teams
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Understanding of IAM, OAuth2, API security, and access control patterns
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Understanding of secure application design principles
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Experience implementing observability using Prometheus, Grafana, and OpenTelemetry
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Strong understanding of logging, monitoring, and production diagnostics
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Ability to collaborate effectively with TPMs on delivery planning and dependency management
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Ability to collaborate with FDEs on customer-driven technical implementations
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Strong understanding of Agile/Scrum engineering workflows
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Experience working in structured sprint-based engineering environments
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Strong technical leadership and engineering decision-making capability
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Strong mentoring and team enablement capability
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Ability to communicate technical trade-offs clearly to both technical and business stakeholders
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Strong problem-solving and root cause analysis mindset
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High ownership and accountability for technical delivery outcomes
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Strong collaboration across cross-functional teams
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Ability to remain effective under delivery pressure and shifting priorities
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Experience supporting AI-first or GenAI application delivery
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Experience with vector databases and semantic retrieval systems
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Experience in enterprise-scale cloud modernization or digital transformation programs
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Experience in telecommunications or other regulated industries
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Experience working in multicultural or distributed teams
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Japanese language proficiency preferred for client-facing or Japan-based roles
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English: Proficiency required
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Japanese: Desirable
