Job Description
One of the top insurance companies, global working environment, let's work together.
■ Job Title
ML & AI Engineer - senior/lead
■ Your Role and Responsibilities
As a Lead ML/AI Engineer, you will be responsible for developing, deploying, and maintaining ML/AI pipelines and infrastructure, leveraging best practices in MLOps, LLMOps, and GenOps to deliver scalable and reliable AI solutions.
You will collaborate with Data Scientists, Data Platform Engineering, and AI Governance teams to operationalize ML/AI models while ensuring compliance with internal AI governance standards.
Additionally, your role will be crucial in delivering business-driven AI solutions by:
Designing and implementing technical solutions that align with business needs and timelines.
Coordinating with technical teams to manage implementation dependencies.
Providing accurate technical estimates and keeping stakeholders updated on progress.
Identifying and mitigating potential risks and blockers early in the process.
Ensuring high-quality standards while meeting delivery deadlines.
Contributing to solution design, balancing technical constraints and business priorities.
Key Responsibilities:
1. ML/AI Pipeline Development & Implementation
Design, build, and maintain ML/AI pipelines using Databricks MLFlow for model training and inference deployment.
Implement MLOps, LLMOps, and GenOps best practices for model deployment, monitoring, and maintenance.
Develop and manage feature stores in accordance with defined architectural patterns.
Establish automated testing frameworks for ML/AI pipelines and models.
Implement monitoring solutions for deployed models, adhering to Group MLOps Index standards.
2. Model Operations & Infrastructure
Deploy and manage ML/AI models in production environments.
Extensive hands-on experience with Infrastructure as Code (IaC), specifically using Terraform for infrastructure provisioning, testing, and optimization.
Configure and fine-tune model serving endpoints while ensuring compliance with security best practices.
Implement caching and performance optimization strategies to enhance model inference efficiency.
Support the integration of LLMs through SecureGPT platform.
Maintain comprehensive documentation for deployed models and ML pipelines.
3. Collaboration & Technical Support
Work closely with Data Scientists to understand model requirements and implementation needs.
Assist domain squads in troubleshooting model deployment and performance issues.
Participate in code reviews and contribute to technical discussions.
Share knowledge and best practices with the Data Lab community focused on ML & AI.
Provide feedback on pipeline improvements and optimization opportunities.
■ Experience and Qualifications
1. Technical Skills & Experience
Bachelors degree in Computer Science, Data Science, or a related technical field.
5+ years of experience in software development, with at least 2+ years focused on ML/AI systems.
Strong programming skills in Python, with hands-on experience in ML frameworks (PyTorch, TensorFlow, Scikit-learn, etc.).
Practical experience with MLOps/LLMOps tools and best practices (MLFlow, Kubeflow).
Experience with Cloud Platforms (AWS preferred) and containerization technologies.
Understanding of CI/CD/CT (Continuous Training), DevOps/ModelOps/DataOps principles, and relevant tools (JIRA, Jenkins).
Familiarity with data processing frameworks and modern data stacks, particularly Cloud SQL Warehouses (Databricks preferred).
Hands-on experience with model monitoring and observability tools.
Knowledge of AI ethics principles and fairness metrics.
Understanding of regulatory frameworks such as GDPR, CCPA, AI Act, etc.
Industry experience in finance/insurance is a plus.
2. Behavioral Competencies
Strong analytical and problem-solving skills.
High attention to detail in implementation and documentation.
Open-minded and collaborative, with a team-oriented attitude.
Effective communication skills for cross-functional collaboration.
Ability to work independently while adhering to established guidelines.
Proactive in identifying and resolving technical issues.
Committed to continuous learning and staying updated on ML/AI advancements.
3. Language Proficiency
Business-level English proficiency required.
Japanese language skills preferred but not mandatory.
■ Benefits
Hybrid work
■ Work Location
Tokyo
■ Salary
~10M
More details will be provided during the meeting.
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Require English】ML
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