Job Title
Lead AI / ML Platform Engineer – Skill Intelligence & Data Platform
Location
Japan
Workplace Type
Remote-Friendly / Flexible Work Arrangement
Industry
HR Technology / EdTech / AI & Data Platforms
Job Category
Machine Learning Engineering / Data Platform Engineering / AI Infrastructure
Career Level
Lead Level
Company Overview
A fast-growing technology company developing talent intelligence and workforce technology solutions is seeking a Lead AI / ML Platform Engineer to drive the evolution of its next-generation skill intelligence platform. The organization leverages AI, machine learning, and large-scale data infrastructure to help businesses better understand, manage, and develop workforce capabilities.
Role Overview
This leadership position is responsible for designing, building, and operating a large-scale Skill Intelligence Platform, including skill ontology frameworks, AI-ready data infrastructure, machine learning platforms, and MLOps environments.
The successful candidate will work closely with business stakeholders, lead data-related initiatives, provide technical direction to data science teams, and establish engineering standards that support long-term product and business growth.
Key Responsibilities
Skill Taxonomy & Ontology Design
Design and maintain enterprise-wide skill taxonomy and ontology frameworks.
Define relationships between skills, competencies, and related workforce data.
Establish governance processes for taxonomy updates and change management.
Develop organization-wide quality standards, usage policies, and data governance practices.
Ensure consistency, accuracy, and scalability of skill intelligence frameworks.
Data Platform Architecture
Lead the design and development of a centralized skill data platform integrating multiple data sources.
Define architecture standards and long-term technical roadmaps.
Build AI-ready data infrastructure capable of supporting advanced analytics and machine learning workloads.
Ensure data reliability, quality, scalability, and maintainability.
Skill Intelligence Analytics
Develop frameworks for analyzing skill relationships and workforce intelligence.
Evaluate and implement analytical models that support product enhancement and business strategy.
Standardize metrics and KPIs used across products and internal stakeholders.
Drive data-driven decision-making throughout the organization.
Machine Learning & Generative AI Development
Lead the design and implementation of machine learning and generative AI capabilities.
Define model architecture standards and best practices.
Establish quality assurance, risk management, and governance frameworks for AI systems.
Support development of skill intelligence applications powered by machine learning and large language models.
MLOps Platform Development
Design, build, and operate scalable MLOps infrastructure.
Implement feature stores, model registries, and experiment management frameworks.
Define best practices for model lifecycle management.
Establish secure and scalable machine learning workflows.
Model Deployment & Infrastructure
Design production-grade model deployment environments.
Build scalable inference infrastructure utilizing Kubernetes and container technologies.
Define CI/CD standards and deployment pipelines for machine learning services.
Support productionization of AI and ML workloads.
Monitoring, Quality Management & Experimentation
Define model monitoring strategies and operational metrics.
Design A/B testing frameworks and experimentation methodologies.
Lead cross-functional quality assurance and performance optimization initiatives.
Establish governance standards for production AI systems.
Project Leadership & Stakeholder Management
Lead technical projects from planning through execution.
Manage priorities, project roadmaps, and delivery schedules.
Collaborate with business leaders to define requirements and success metrics.
Provide guidance and mentorship to data scientists and engineering teams.
Drive cross-functional alignment across technical and business organizations.
Required Qualifications
Technical Experience
5+ years of professional experience developing data platforms and solutions using Python.
Strong experience designing and implementing scalable data architectures.
Experience building production-grade data pipelines and analytics platforms.
Strong software engineering and system design capabilities.
Preferred Qualifications
Machine Learning & AI
Experience designing, developing, and operating ML pipelines in cloud environments.
Practical experience with MLOps frameworks and tools.
Experience managing machine learning lifecycle processes from training through deployment.
Knowledge of generative AI and large language model applications.
Experience with MLflow and experiment management platforms.
Data Engineering
Experience working with enterprise data warehouses and data platforms.
Experience in data extraction, transformation, preprocessing, and feature engineering.
Experience designing and operating large-scale data pipelines.
Cloud & Infrastructure
Experience with public cloud platforms.
Experience with Kubernetes and containerized environments.
Experience implementing CI/CD pipelines and automation frameworks.
Knowledge of IAM and cloud security best practices.
Model Deployment & Operations
Experience building and deploying model APIs.
Experience managing container images and deployment pipelines.
Experience implementing model monitoring, A/B testing, and quality assurance processes.
Leadership & Collaboration
Experience leading AI, machine learning, or data platform initiatives.
Experience collaborating with business stakeholders on requirements and prioritization.
Experience guiding data science teams and productionizing research outputs.
Preferred Certifications
Cloud and Data Engineering certifications (AWS, Google Cloud, Azure)
Machine Learning and Data Science certifications
Kubernetes certifications (CKA)
Project Management certifications (PMP)
Information Systems and Database certifications
Technology Environment
Python
Kubernetes
Public Cloud Platforms
CI/CD Pipelines
IAM (Identity & Access Management)
MLflow
Model Registry
Feature Store
Data Warehouses (DWH)
Data Pipeline Technologies
Ideal Candidate Profile
Strong communicator capable of influencing both technical and business stakeholders.
Able to bridge business strategy and technical execution.
Comfortable creating structure and governance within ambiguous environments.
Passionate about organizational development and team growth.
Strong ownership mindset with a focus on long-term impact.
Interested in building AI-driven products and next-generation data platforms.
Collaborative leader who enjoys mentoring and developing others.
What Makes This Opportunity Attractive
Strategic Business Impact
Direct collaboration with senior leadership and business executives.
Opportunity to influence company-wide data and AI strategy.
Large-Scale AI & Data Infrastructure
Lead the creation of advanced AI-ready data platforms and enterprise-scale MLOps environments.
Drive initiatives spanning multiple products, business units, and external data ecosystems.
Technical Leadership
Define engineering standards and technical direction for a specialized AI and data organization.
Shape platform architecture, governance frameworks, and operational best practices.
Flexible Working Environment
Remote-friendly work style with flexibility in work location within Japan.
Opportunity to work with highly technical teams focused on emerging AI and machine learning technologies.