Data Scientist

We are seeking an experienced and highly technical Senior AI FDE to join our customer-facing consulting team. This remote role requires a blend of advanced Generative AI and Machine Learning (ML) expertise, deep knowledge of production MLOps/AIOps principles, and a proven track record in client-facing implementation. A successful candidate will be instrumental in designing, deploying, and maintaining production-grade Generative and Agentic AI solutions and traditional machine learning models for our diverse client base.

Key Responsibilities

• Technical Leadership & Client Engagement: Serve as a primary technical consultant, leading and executing end-to-end AI/ML project implementations directly with clients, translating complex business problems into robust technical solutions. Exhibit excellent communication, presentation, and stakeholder management skills to clearly articulate technical findings, proposals, and project status to both technical and non-technical audiences.
• MLOps & AIOps Pipelines: Design, build, and maintain production-grade AI and ML pipelines, focusing on continuous integration, continuous delivery (CI/CD), and advanced MLOps/AIOps practices to ensure reliability and scalability of models.
• Generative & Agentic AI Implementation: Implement and optimize cutting-edge Generative and Agentic AI applications, demonstrating hands-on experience with technologies like multi-agent orchestration, agent evaluation, and Retrieval Augmented Generation (RAG) Systems in a production setting.
• System Architecture: Architect end-to-end AI/ML systems, designing both traditional Machine Learning- and Generative AI-based solutions based on customer requirements.
• Infrastructure Management: Manage underlying solution infrastructure necessary for production deployment, demonstrating proficiency in technologies such as Docker, pipeline orchestrators, and cloud ecosystem integration.
• Data & Distributed Computing: Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high-performance data processing (e.g., using technologies like Apache Spark).
• Practice Growth & Documentation: Contribute to the strategic growth of the AI FDE Practice, including participation in technical assignments and knowledge transfer activities. Ensure all client engagements and training activities are properly documented and reported via designated partner platforms.Candidate Experience & Required Qualifications
• An ideal candidate for Advantest will have the following experience:
• Agentic AI & Azure AI Foundry (Must): Practical experience in building AI agents, agentic workflows, and experimenting with Azure AI Foundry to solve real-world problems.
• General Microsoft Stack (Must): Practical experience navigating and integrating solutions across Microsoft’s cloud, data, and enterprise application ecosystem.
• Modern Gen AI Foundations (Must): Understanding of multi-agent orchestration, RAG architectures, MCP, text-to-SQL architecture, and action-oriented AI.Professional Experience:
• 4+ years of hands-on professional experience developing, deploying, and managing Generative/Agentic AI or Machine Learning systems, with a mandatory requirement for productionizing and maintaining models in a live environment.
• Consulting & Solutions Architecture: 3+ years of experience in a customer-facing consulting or solutions architect role, focused on technical implementation and delivery.
• Communication: Excellent verbal and written communication skills for effective client and internal team interaction.Technical Core:
• Expertise in Python for data-intensive and scalable AI/ML applications.
• Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment best practices.
• Demonstrable experience with infrastructure management, encompassing containerization (Docker) and data pipeline orchestration.
• Demonstrated commitment to continuous learning in emerging AI/ML fields.

We are seeking an experienced and highly technical Senior AI FDE to join our customer-facing consulting team. This remote role requires a blend of advanced Generative AI and Machine Learning (ML) expertise, deep knowledge of production MLOps/AIOps principles, and a proven track record in client-facing implementation. A successful candidate will be instrumental in designing, deploying, and maintaining production-grade Generative and Agentic AI solutions and traditional machine learning models for our diverse client base.

Key Responsibilities

• Technical Leadership & Client Engagement: Serve as a primary technical consultant, leading and executing end-to-end AI/ML project implementations directly with clients, translating complex business problems into robust technical solutions.
• Exhibit excellent communication, presentation, and stakeholder management skills to clearly articulate technical findings, proposals, and project status to both technical and non-technical audiences.
• MLOps & AIOps Pipelines: Design, build, and maintain production-grade AI and ML pipelines, focusing on continuous integration, continuous delivery (CI/CD), and advanced MLOps/AIOps practices to ensure reliability and scalability of models.
• Generative & Agentic AI Implementation: Implement and optimize cutting-edge Generative and Agentic AI applications, demonstrating hands-on experience with technologies like multi-agent orchestration, agent evaluation, and Retrieval Augmented Generation (RAG) Systems in a production setting.
• System Architecture: Architect end-to-end AI/ML systems, designing both traditional Machine Learning- and Generative AI-based solutions based on customer requirements.
• Infrastructure Management: Manage underlying solution infrastructure necessary for production deployment, demonstrating proficiency in technologies such as Docker, pipeline orchestrators, and cloud ecosystem integration.
• Data & Distributed Computing: Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high-performance data processing (e.g., using technologies like Apache Spark).
• Practice Growth & Documentation: Contribute to the strategic growth of the AI FDE Practice, including participation in technical assignments and knowledge transfer activities. Ensure all client engagements and training activities are properly documented and reported via designated partner platforms.

Candidate Experience & Required Qualifications

• An ideal candidate for Advantest will have the following experience:
• Agentic AI & Azure AI Foundry (Must): Practical experience in building AI agents, agentic workflows, and experimenting with Azure AI Foundry to solve real-world problems.
• General Microsoft Stack (Must): Practical experience navigating and integrating solutions across Microsoft’s cloud, data, and enterprise application ecosystem.
• Modern Gen AI Foundations (Must): Understanding of multi-agent orchestration, RAG architectures, MCP, text-to-SQL architecture, and action-oriented AI.Professional Experience:
• 4+ years of hands-on professional experience developing, deploying, and managing Generative/Agentic AI or Machine Learning systems, with a mandatory requirement for productionizing and maintaining models in a live environment.
• Consulting & Solutions Architecture: 3+ years of experience in a customer-facing consulting or solutions architect role, focused on technical implementation and delivery.
• Communication: Excellent verbal and written communication skills for effective client and internal team interaction.Technical Core:
• Expertise in Python for data-intensive and scalable AI/ML applications.
• Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment best practices.
• Demonstrable experience with infrastructure management, encompassing containerization (Docker) and data pipeline orchestration.
• Demonstrated commitment to continuous learning in emerging AI/ML fields.

Preferred Qualifications

• Databricks (Preferred): Prior hands-on experience with the Databricks platform, especially for Generative and Agentic AI in production workflows.
• Large-Scale Data Processing: Knowledge of specific tools and techniques used in large-scale data processing and lakehouse database architectures.
• Databricks (Preferred): Prior hands-on experience with the Databricks platform, especially for Generative and Agentic AI in production workflows.
• Large-Scale Data Processing: Knowledge of specific tools and techniques used in large-scale data processing and lakehouse database architectures.