At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age.

With approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs).

Generative AI Engineer – NLP & Machine Learning Specialist 3-6 year of Exp.
 Strong Expertise
    
- Generative AI Model Development
- Natural Language Processing (NLP)
- Machine Learning & Deep Learning
- Python Programming for AI Development
- Model Fine-Tuning & Optimization
- AI Model Deployment & MLOps (Docker, Kubernetes, CI/CD)
- Data Science & Statistics

Basic Proficiency
    
- Large Language Models (LLMs) & Open-Source AI Frameworks
- Data Engineering & Data Processing (Apache Spark, Pandas, NumPy, PyTorch, Scikit-learn, TensorFlow)
- Conversational AI & Chatbot Development (LangChain, AutoGen)
- Cloud AI Platforms (GCP, AWS, Azure)

Knowledge Only
    
- Open-Source Contributions in AI
- Software Design Principles & Architecture
- AI Ethics & Bias Mitigation
 
 
2. Primary Skills
Generative AI Model Development
Design, develop, and deploy state-of-the-art generative AI models, including open-source LLMs, tailored for specific business needs.
Natural Language Processing (NLP)
Implement advanced NLP techniques such as text generation, summarization, translation, and sentiment analysis for AI-driven solutions.
Machine Learning & Deep Learning
Apply cutting-edge ML and deep learning algorithms to enhance AI model accuracy and efficiency in real-world applications.
Python Programming for AI Development
Strong proficiency in Python (or R/Java) for developing and implementing AI models, leveraging frameworks like Hugging Face, OpenAI GPT, spaCy, and NLTK.
Model Fine-Tuning & Optimization
Customize pre-trained AI models for domain-specific use cases, optimizing them for performance, scalability, and efficiency.
AI Model Deployment & MLOps
Develop, deploy, and maintain AI models in production environments using FastAPI, Django, and MLOps tools such as Docker, Kubernetes, and CI/CD pipelines.
 
3. Secondary Skills
Large Language Models (LLMs) & Open-Source AI Frameworks
Experience with LangChain, AutoGen, and other frameworks to build scalable AI solutions that leverage large-scale pre-trained models.
Data Engineering & Data Processing
Work with data processing frameworks such as Apache Spark, Pandas, PyTorch, NumPy, Scikit-learn, and TensorFlow to prepare high-quality training datasets.
Conversational AI & Chatbot Development
Develop intelligent chatbots and conversational AI applications using NLP techniques, integrating with business applications.
Cloud AI Platforms (GCP, AWS, Azure)
Strong knowledge of cloud platforms to deploy and scale AI applications efficiently in cloud environments.
Open-Source Contributions in AI
Actively contribute to open-source AI projects, improving and innovating existing LLMs and generative AI technologies.
AI Ethics & Bias Mitigation
Awareness of AI fairness, ethical considerations, and techniques to mitigate biases in generative AI models.