Intern AI Solution Engineer (Japanese)

Responsibilities

Pre-Sales & Solution Support:

• Partner with the Solution Architect team to understand and analyze business problem statements from clients.
• Assist in pre-sales activities, including technical proposals, proof-of-concepts (PoCs), and solution demos.
• Research and develop AI-driven prototypes, showcasing the capabilities of NLP, Large Language Models (LLMs), and Cloud AI solutions.

Solution Development & Implementation:

• Work with senior engineers to design and deliver AI-powered software solutions that align with business needs.
• Implement and fine-tune NLP models for real-world applications such as chatbots, document automation, and intelligent search.
• Deploy AI solutions on cloud platforms (Azure, AWS, GCP), ensuring scalability, security, and efficiency.
• Support in integrating AI models with enterprise systems and APIs.

Collaboration & Knowledge Sharing:

• Work in cross-functional teams, collaborating with developers, data scientists, and pre-sales engineers.
• Proactively participate in discussions, presenting ideas clearly in English to both technical and non-technical stakeholders.
• Stay up to date with the latest AI trends, continuously exploring ways to enhance our solution offerings.

Requirements

• Natural Language Processing
• Data Engineering
• Large Language Model
• Data Engineer
• Japanese
• English
• PostgreSQL
• Python
• MongoDB

Must-Have:

• Certified minimum N3.
• Final-year student or recent graduate in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Strong knowledge of NLP, including Large Language Models (LLMs), OpenAI, Hugging Face, or LangChain.
• Solid experience in software engineering (Python, JavaScript, or related programming languages).
• Proactive and confident speaker, with fluent English communication skills (both verbal and written).
• Strong analytical and problem-solving skills, with a mindset to turn complex business problems into AI-driven solutions.
• A self-starter, eager to learn and take initiative in a fast-paced environment.

Nice-to-Have:

• Hands-on experience with MLOps tools for AI model lifecycle management.
• Familiarity with Cloud Computing (Azure, AWS, GCP) and AI model deployment.

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